This research focuses on developing smart techniques for automatically detecting and fixing errors in programs written in various programming languages. It uses machine learning (ML) and natural language processing (NLP) methods to analyze source code, find syntax or runtime errors, and provide precise correction suggestions. By studying different programming structures and common error patterns, the project aims to make code debugging faster, more efficient, and more reliable. The results of this study can support the creation of advanced compiler systems and interactive learning tools for programmers. Overall, this work combines intelligent automation with practical coding support to build more accurate, responsive, and user-friendly programming environments.