Maximizing the effectiveness of nonlinear control techniques (NCT) is crucial for the broad acceptance and smooth integration of autonomous vehicles. Navigating the intricacies of real-world settings, boosting vehicle stability, and enhancing overall performance are all greatly impacted by these tactics. For manufacturers to successfully deploy autonomous cars in environments that are both dynamic and unpredictable, it is crucial to strike a balance between rigid control and flexibility. Autonomous vehicles confront a number of obstacles when trying to optimize nonlinear control systems, including changing road conditions, unpredictable traffic, and the requirement to make decisions in real-time. Because of the complexity of these problems, traditional control methods are frequently not up to the task, thus one need to find new ways to make control strategies more resilient and flexible. A Nonlinear Control Optimization Framework (NCOF) is suggested in this research; it makes use of adaptive control mechanisms, high-level optimization methods, and machine learning approaches. NCOF is engineered to enhance the vehicle's performance in a variety of situations by continuously adapting and optimizing control parameters using environmental data and real-time feedback. Beyond conventional navigation, the NCOF finds use in situations including emergency response, complicated urban surroundings, and bad weather. Improved decision-making, navigating, and passenger safety in a variety of situations are all outcomes of NCOF's optimization of nonlinear control techniques for autonomous cars. Thorough simulation assessments are carried out across several virtual scenarios to verify the efficacy of the suggested NCOF. Displaying the flexibility, stability, and efficacy of NCOF in various contexts, the simulations evaluate the optimal nonlinear control schemes' effectiveness in contrast to conventional methods.