Machine learning plays a pivotal role in enabling self-driving cars to interpret data from various sensors and navigate effectively. These vehicles utilize algorithms to process information from cameras, LiDAR, and radar to identify road conditions, obstacles, and traffic signals. Through continuous learning from real-world experiences, machine learning enhances a vehicle's ability to make decisions in dynamic environments, improving safety and efficiency. As these systems evolve, they aim to reduce human error and optimize driving patterns, making autonomous transportation increasingly viable and reliable.