Accurate perception in autonomous vehicles is critical for ensuring safety and reliability in complex driving environments. Conventional single-sensor or single-model approaches often fail in the presence of occlusions, low visibility, or dense traffic. This work introduces an advanced multi-sensor deep learning framework that integrates YOLOv8 for object detection, Cascade R-CNN for precise localization, DeepLabv3+ for semantic segmentation, BEVFusion for multi-modal data fusion, and DeepSORT for temporal tracking. By combining camera, LiDAR, and radar inputs, the framework establishes a robust pipeline capable of addressing adverse conditions. Experimental evaluations on benchmark datasets, including KITTI, nuScenes, and ApolloScape, demonstrate significant improvements in accuracy, recall, intersection-over-union, and multi-object tracking performance. The proposed architecture represents a reliable step toward safe and scalable autonomous driving systems.