Autonomous driving technologies, which allow cars to function without hu-man involvement, are revolutionizing the transportation sector. Different types of sensors, such as cameras, LIDAR, and radar, combined with advanced control algorithms are incorporated in this system to navigate and manage critical vehicle functions. Despite this, autonomous vehicles (AVs) are susceptible to security vulnerabilities, including sensor spoofing. Sensor spoofing involves manipulating sensor data to deceive the vehicle’s view of its environment. This paper suggests a thorough security framework that brings together hardware authentication methods, sensor validation strategies using Machine Learning and secure Vehicle-to- Everything (V2X) communication. This strategy guarantees the integrity of sensor data, reducing the dangers of cyberattacks on autonomous driving systems. Moreover, a comparison and evaluation of current security techniques are given, which demonstrates the improvements made by the suggested approach. The findings show enhanced detection of sensor spoofing, which helps to maintain the communication smoothly.