A basic problem in software development is code optimization, which improves execution accuracy and efficiency. However, optimizing techniques often result in unanticipated errors that impact program performance and dependability. Static analysis, heuristic parameter adjustment, and machine learning-based models are the classic approaches used to minimize failures; however, these approaches are less flexible and less explicable. In this research, we propose a knowledge engineering-based method that integrates machine learning, expert systems, and rule-based reasoning to minimize errors in code optimization. To identify and fix optimization-related faults, our approach makes use of constraint-based error detection, adaptive learning models, and semantic knowledge representation. We look at common sources of errors, talk about existing optimization techniques, and explore how knowledge engineering techniques enhance error detection and rectification. In addition, we present hybrid AI-based approaches mixing symbolic reasoning with deep learning to enhance optimization accuracy. Experimental analyses show that our framework improves compiler efficiency, minimizes optimization-caused errors, and enhances overall software reliability. This research gives insights into the future of intelligent and self-correcting optimization methods, closing the gap between code efficiency and correctness of words.