diff --git a/aidrin/file_handling/file_parser.py b/aidrin/file_handling/file_parser.py
index 88009ddf..79311d73 100644
--- a/aidrin/file_handling/file_parser.py
+++ b/aidrin/file_handling/file_parser.py
@@ -6,6 +6,7 @@
 from aidrin.file_handling.readers.hdf5_reader import hdf5Reader
 from aidrin.file_handling.readers.json_reader import jsonReader
 from aidrin.file_handling.readers.npz_reader import npzReader
+from aidrin.file_handling.readers.root_reader import rootReader
 
 # Notes:
 # To add support for new file types:
@@ -21,6 +22,7 @@
     ".xls, .xlsb, .xlsx, .xlsm": excelReader,
     ".json": jsonReader,
     ".h5": hdf5Reader,
+    ".root": rootReader,
     # Add additional file types here
 }
 
@@ -31,6 +33,7 @@
     (".json", "JSON"),
     (".npz", "NumPy"),
     (".h5", "HDF5"),
+    (".root", "ROOT"),
     # Add additional file types here using the format:
     # (file_type,file_type_name)
 ]
diff --git a/aidrin/file_handling/readers/root_reader.py b/aidrin/file_handling/readers/root_reader.py
new file mode 100644
index 00000000..5894d014
--- /dev/null
+++ b/aidrin/file_handling/readers/root_reader.py
@@ -0,0 +1,158 @@
+import pandas as pd
+from aidrin.file_handling.readers.base_reader import BaseFileReader
+
+try:
+    import uproot
+    UPROOT_AVAILABLE = True
+except ImportError:
+    UPROOT_AVAILABLE = False
+
+
+class rootReader(BaseFileReader):
+    # ROOT is a file format developed by CERN for particle physics data
+    # it stores data in a tree structure with branches and leaves
+    # uproot is the python library that reads ROOT files without needing
+    # the full ROOT framework installed which is very heavy
+    # i learned about this format while looking at the gsoc project description
+    # which specifically mentioned ROOT as one of the formats to add
+
+    def read(self):
+        if not UPROOT_AVAILABLE:
+            self.logger.error(
+                "uproot and awkward are required to read ROOT files. "
+                "install them with: pip install uproot awkward"
+            )
+            return None
+
+        try:
+            rows = []
+
+            with uproot.open(self.file_path) as root_file:
+                # getting all keys in the root file
+                all_keys = root_file.keys()
+                self.logger.info(f"ROOT file opened. Keys found: {all_keys}")
+
+                for key in all_keys:
+                    obj = root_file[key]
+
+                    # handling TTree objects - main data structure in ROOT
+                    if isinstance(obj, uproot.behaviors.TTree.TTree):
+                        self.logger.info(
+                            f"reading TTree: {key} with "
+                            f"{obj.num_entries} entries"
+                        )
+                        try:
+                            # converting tree to pandas dataframe
+                            # using uproot's built in pandas conversion
+                            df_tree = obj.arrays(library="pd")
+
+                            # flattening any nested columns
+                            for col in df_tree.columns:
+                                if df_tree[col].dtype == object:
+                                    try:
+                                        df_tree[col] = df_tree[col].apply(
+                                            lambda x: x[0] if hasattr(x, '__len__')
+                                            and len(x) == 1 else str(x)
+                                        )
+                                    except Exception:
+                                        df_tree[col] = df_tree[col].astype(str)
+
+                            for _, row in df_tree.iterrows():
+                                rows.append(row.to_dict())
+
+                        except Exception as e:
+                            self.logger.warning(
+                                f"could not read TTree {key}: {e}"
+                            )
+
+                    # handling TH1 histograms
+                    elif hasattr(obj, 'values') and hasattr(obj, 'axes'):
+                        try:
+                            values = obj.values()
+                            axes = obj.axes()
+
+                            if len(axes) == 1:
+                                # 1d histogram
+                                bin_centers = (axes[0][:-1] + axes[0][1:]) / 2
+                                for center, val in zip(bin_centers, values):
+                                    rows.append({
+                                        f"{key}_bin_center": float(center),
+                                        f"{key}_value": float(val)
+                                    })
+                            self.logger.info(f"read histogram: {key}")
+
+                        except Exception as e:
+                            self.logger.warning(
+                                f"could not read histogram {key}: {e}"
+                            )
+
+            if not rows:
+                self.logger.warning("no data found in ROOT file")
+                return None
+
+            df = pd.DataFrame(rows)
+            self.logger.info(
+                f"ROOT file read successfully, shape: {df.shape}"
+            )
+            return df
+
+        except Exception as e:
+            self.logger.error(f"error reading ROOT file: {e}")
+            return None
+
+    def parse(self):
+        # returns all tree and histogram names in the ROOT file
+        if not UPROOT_AVAILABLE:
+            self.logger.error("uproot is required to parse ROOT files")
+            return None
+
+        try:
+            with uproot.open(self.file_path) as root_file:
+                keys = root_file.keys()
+                self.logger.info(f"ROOT file keys: {keys}")
+                return keys
+
+        except Exception as e:
+            self.logger.error(f"error parsing ROOT file: {e}")
+            return None
+
+    def filter(self, kept_keys):
+        # filters ROOT file to keep only specified trees or histograms
+        if not UPROOT_AVAILABLE:
+            self.logger.error("uproot is required to filter ROOT files")
+            return None
+
+        try:
+            if isinstance(kept_keys, str):
+                kept_keys = [k.strip() for k in kept_keys.split(',')]
+
+            kept_keys = set(kept_keys)
+            rows = []
+
+            with uproot.open(self.file_path) as root_file:
+                for key in root_file.keys():
+                    # stripping cycle number from key name
+                    # root keys have format "name;cycle"
+                    clean_key = key.split(';')[0]
+
+                    if clean_key in kept_keys:
+                        obj = root_file[key]
+                        if isinstance(obj, uproot.behaviors.TTree.TTree):
+                            try:
+                                df_tree = obj.arrays(library="pd")
+                                for _, row in df_tree.iterrows():
+                                    rows.append(row.to_dict())
+                            except Exception as e:
+                                self.logger.warning(
+                                    f"could not filter TTree {key}: {e}"
+                                )
+
+            if not rows:
+                self.logger.warning("no data found after filtering")
+                return None
+
+            return pd.DataFrame(rows)
+
+        except Exception as e:
+            self.logger.error(f"error filtering ROOT file: {e}")
+            return None