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Urbanization and the rise in vehicle ownership have intensified the need for efficient parking solutions, especially in densely populated cities. Traditional parking systems lack automation and often result in time-consuming searches for vacant spots, increased traffic congestion, and driver frustration. This project addresses these challenges by introducing a smart parking lot navigation system that leverages the power of computer vision and artificial intelligence to provide real-time detection and navigation to available parking slots. The proposed system uses the YOLOv8 (You Only Look Once, version 8) object detection model to accurately identify and classify parking slots as either "occupied" or "empty" from static images or real-time video feeds. Once the available slots are detected, the system employs the A* pathfinding algorithm to determine the shortest and most efficient route for the vehicle to reach the nearest available spot. A user-friendly interface built using Streamlit allows users to upload images, visualize slot status, and receive navigation guidance interactively. This intelligent solution not only optimizes parking lot utilization but also significantly enhances user convenience by reducing search time and fuel consumption. The system is scalable and adaptable for integration into smart cities and IoT-based infrastructures. By combining real-time computer vision with path planning algorithms, the project demonstrates a practical application of AI in urban mobility and represents a step forward in the development of autonomous and intelligent urban transport systems.