Breast cancer remains a leading cause of cancer-related mortality among women globally, where early and accurate diagnosis significantly improves treatment outcomes [1]. Traditional histopathological diagnosis methods are time-intensive, subjective, and exhibit considerable inter-observer variability [2]. While machine learning approaches show promise for automated cancer detection, conventional classifiers lack sophisticated feature extraction capabilities for complex medical images, and standalone Convolutional Neural Networks (CNNs) frequently suffer from overfitting on limited datasets [3]. This study proposes a novel hybrid CNN-Random Forest model that leverages the hierarchical feature learning capabilities of CNNs combined with the robust ensemble classification power of Random Forest algorithms. The proposed system addresses key challenges, including limited dataset size, class imbalance, and multi-magnification image variability through advanced transfer learning and data augmentation techniques [4]. Extensive experiments on the BreakHis dataset demonstrate that the hybrid model achieves superior classification accuracy of 96.7%, outperforming traditional Random Forest approaches (∼88−90%) and existing standalone CNN models (∼92−94%) [9]. The architecture employs pretrained CNN models for deep feature extraction while utilizing Random Forest's interpretability and generalization capabilities for final classification. Cross-validation performance evaluation reveals superior metrics, including accuracy (96.3%), recall (97.1%), F1-score (96.5%), and AUC-ROC (0.981). This approach offers enhanced robustness, reduced overfitting, and improved clinical interpretability, making it a viable computer-aided diagnostic tool for breast cancer detection in histopathological imaging.