Large-scale software projects frequently encounter the challenge of manually propagating code changes across branches—a process that is error-prone due to code divergence, conflicting dependencies, and branch-specific modifications. Automating code porting can streamline development workflows, accelerate development cycles, and improve team collaboration. However, achieving this automation presents significant hurdles, particularly in maintaining consistency and resolving conflicts during codebase integration. We propose an approach that integrates rule-based analysis with artificial intelligence-driven code generation to automate the identification of porting requirements and the development of ‘context-aware’ modifications. Our comprehensive, end-to-end framework starts by extracting recent commits to evaluate divergence. It subsequently assesses the necessity for porting changes and employs large language model (LLM) based systems to generate adaptive code suggestions tailored to files exhibiting inconsistencies. Experimental results suggest a substantial decrease in manual work through pipeline-generated pull requests. Despite these promising outcomes, integrating LLMs into complex workflows presents challenges, such as handling intricate dependencies and ensuring alignment with a company’s software development issue tracking and change management systems. This paper explores the potential and limitations of LLMs in advancing automation within software engineering and suggests future directions for enhancing these models to achieve industry-grade reliability.