This study presents an advanced path-tracking framework for autonomous vehicles, integrating visual perception, behavior analysis, and the Adaptive Neuro-Fuzzy Inference System (ANFIS) to enhance trajectory control in dynamic environments. By leveraging real-time data from visual sensors, the system captures critical environmental information, while behavior analysis evaluates vehicle dynamics and external influences. ANFIS synergizes neural networks and fuzzy logic to optimize trajectory predictions, ensuring adaptability and precision under varying conditions. The proposed data-driven approach incorporates feature extraction, dimensionality reduction, and hybrid learning techniques to improve computational efficiency. Experimental validation across urban and highway scenarios demonstrates a substantial performance gain over conventional methods, achieving a 96.5% tracking accuracy and a response time of 0.35 seconds. The results underscore the system’s ability to seamlessly adjust to environmental changes, making it a promising solution for real-world autonomous navigation. Future research will explore deep learning integration to further optimize performance and expand applicability.