diff --git a/src/models/svm_classifier.py b/src/models/svm_classifier.py
index 92f05a3..96124d3 100644
--- a/src/models/svm_classifier.py
+++ b/src/models/svm_classifier.py
@@ -1,6 +1,8 @@
 from sklearn.svm import SVC
 from src.models.base_classifier import BaseModel
 from src.config import get_config
+import numpy as np
+import matplotlib.pyplot as plt
 
 class SVMClassifier(BaseModel):
     """
@@ -30,4 +32,62 @@ def _build_model(self):
             probability=self.params.get('probability', default_params.get('probability', True)),
             class_weight=self.params.get('class_weight', default_params.get('class_weight', None)),
             random_state=self.params.get('random_state', default_params.get('random_state', 42))
-        )
\ No newline at end of file
+        )
+
+    def plot_decision_boundary(self, X, y, title="SVM Decision Boundary"):
+        """
+        Visualizes the decision boundary, margins, and support vectors.
+        
+        NOTE: This method strictly requires 2D feature data.
+        """
+        if self.model is None:
+            raise RuntimeError("Model not trained. Call train() first.")
+
+
+        X = np.array(X)
+        y = np.array(y)
+
+        # Strict dimension check
+        if X.shape[1] != 2:
+            raise ValueError(
+                f"Cannot visualize SVM boundary with {X.shape[1]} dimensions. "
+                "Tip: Use PCA to reduce to 2D or select only 2 columns."
+            )
+
+        h = 0.02  # Step size
+        x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
+        y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
+        xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
+                             np.arange(y_min, y_max, h))
+
+        plt.figure(figsize=(10, 6))
+
+        # Calculate distance to hyperplane (decision_function)
+        Z = self.model.decision_function(np.c_[xx.ravel(), yy.ravel()])
+        Z = Z.reshape(xx.shape)
+
+        plt.contourf(xx, yy, Z, levels=[-100, 0, 100], alpha=0.2, colors=['#FF9999', '#9999FF'])
+        
+        # Plot key lines:
+        # Level -1 : Negative class margin (dashed)
+        # Level  0 : Decision Boundary (solid)
+        # Level  1 : Positive class margin (dashed)
+        contours = plt.contour(xx, yy, Z, levels=[-1, 0, 1], 
+                               linestyles=['--', '-', '--'], 
+                               colors='k', 
+                               linewidths=[1, 2, 1])
+        
+        plt.clabel(contours, inline=True, fontsize=10, fmt='%1.0f')
+
+        plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.bwr, edgecolors='k', s=60)
+        
+        sv = self.model.support_vectors_
+        plt.scatter(sv[:, 0], sv[:, 1], s=200,
+                    linewidth=1.5, facecolors='none', edgecolors='k', label='Support Vectors')
+
+        plt.title(title)
+        plt.xlabel('Feature 1')
+        plt.ylabel('Feature 2')
+        plt.legend(loc="upper right")
+        plt.grid(False)
+        plt.show()
\ No newline at end of file