Self-driving cars are a popular topic in the field of artificial intelligence and autonomous systems. Self-driving cars have the potential to significantly improve transportation, by increasing safety, efficiency, and convenience. In addition, self-driving cars could make transportation more accessible for people who are unable to drive, such as the elderly or people with disabilities. One approach to designing and implementing a self-driving car is to use neural networks and genetic algorithms. In this project, the use of neural networks and genetic algorithms for self-driving cars is explored, and the effectiveness of this approach through simulations and experiments are demonstrated. By using neural networks to process sensor data and make decisions, and by using genetic algorithms to optimize the weights and biases of the network, it may be possible to improve the overall performance of the self-driving car. Self-driving cars are a technology with the potential to significantly improve transportation and mobility for people around the world. Some potential applications and areas where this technology could be useful include: Personal transportation, Public Transportation, Delivery Services, Ride-sharing. Overall, the potential applications of self-driving cars are wide-ranging and diverse, and will likely continue to evolve as the technology develops and becomes more widespread.