This research presents Autonomous Evolutionary Control Learning, a novel framework that combines PID control, reinforcement learning, and evolutionary optimization to enhance the autonomy and adaptability of robotic systems. Unlike traditional control methods, which often require extensive manual tuning and struggle with dynamic environments, AECL enables robots to refine their behavior in real-time. By integrating deep reinforcement learning with evolutionary algorithms, the system continuously optimizes its parameters, allowing it to respond more effectively to unforeseen disturbances and variations in operational conditions. To validate its performance, AECL was implemented on a four-degree-of-freedom robotic arm equipped with servomotors and an accelerometer. The results demonstrated a significant reduction in positioning errors compared to conventional PID controllers, as well as improved stability and adaptability. One of the key advantages of AECL is its ability to minimize the need for manual recalibration, making it particularly valuable for applications where precision and reliability are critical. Beyond robotics, the principles behind AECL have the potential to benefit a wide range of fields, from industrial automation to medical robotics and autonomous exploration. By enabling robots to learn and evolve without constant human intervention, this approach paves the way for more intelligent, resilient, and efficient systems. Future research will focus on improving computational efficiency and exploring new ways to enhance the system’s ability to generalize across different tasks and environments.