This dissertation addresses the development of an IoT-enabled smart car system capable of autonomous navigation and obstacle management within a simulated urban environment. The primary objective was to design a smart car that can follow a predefined path marked by black tape, detect road forks, and avoid both static and dynamic obstacles in real-time. The methodology adopted included an extensive literature review, iterative design and implementation phases, and rigorous testing in controlled environments. The smart car was equipped with line-tracking sensors, ultrasonic sensors, and a high-definition camera, integrated with advanced algorithms for sensor data processing and decision-making. The project utilized a quantitative research approach to systematically measure and analyze the performance of the smart car under various conditions. Key findings demonstrate that the smart car achieved a high navigation accuracy with a 98% success rate in controlled environments, deviating an average of 1.5 cm from the path. Under varied lighting conditions, the success rate slightly decreased to 95%, with a deviation of 2.0 cm, indicating the need for adaptive sensor calibration. The system effectively detected and avoided static obstacles with a 95% success rate and dynamic obstacles with a 90% success rate, highlighting challenges with high-speed objects. The response times were within acceptable limits, averaging 50 ms in controlled settings, 60 ms under varied lighting, and 70 ms with obstacles. The results validate the smart car’s potential for practical applications, emphasizing the effectiveness of integrating line-tracking sensors, ultrasonic sensors, and cameras for autonomous navigation. However, the study also identified areas for improvement, particularly in handling dynamic obstacles and adapting to varied lighting conditions. Future research should focus on enhancing sensor calibration, developing more sophisticated algorithms for dynamic obstacle avoidance, and conducting outdoor tests to evaluate performance in real-world conditions. In conclusion, this project contributes to the advancement of autonomous vehicle technology by providing a robust framework for developing smart car systems, offering valuable insights into sensor integration, real-time data processing, and adaptive decision-making algorithms. These findings have significant implications for the future of intelligent transportation systems, emphasizing the need for continuous research and development in this rapidly evolving field.