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The rapid advancement of software development tools that elevates productivity, refines code quality, diminishes human error. This project confronts these challenges by integrating artificial intelligence (AI) to automate the analysis of the developer patterns and provide appropriate code suggestions. By harnessing machine learning algorithms, natural language processing and making the most of the Qwen 2.5 coder, we can assiduously survey individual coding habits, study recurring patterns, and detect inefficiencies in real-time. This AI-driven framework tailors itself to unique coding styles while mining insights from diverse repositories of open-source code to generate contextually optimized solutions. Key features comprise of semantic code analysis, predictive pattern recognition, and automated refactoring suggestions, all designed to streamline workflows and elevate consistent, high quality software enhancement. This work ensures for scalable, intelligent coding assistant that empowers developers across diverse programming environments.