The heavy engineering sector, as demonstrated by the link frame mechanical press industry, encounters significant challenges when it comes to optimizing material removal rates (MRR) on horizontal boring machines. These challenges stem from low productivity rates and the production of variable, tailor-made products. In response, this research paper proposes a novel framework leveraging artificial intelligence (AI) technologies to enhance MRR on horizontal boring machines. Drawing on the capabilities of AI, particularly in data-driven decision-making and process optimization, the framework integrates AI algorithms with information communications, manufacturing technologies, and product-related expertise. By harnessing AI’s ability to analyze large datasets, optimize machining sequences, and reduce machining time, the framework aims to improve MRR, increase productivity, and enhance operational efficiency in heavy engineering industries. The paper discusses the key challenges faced by these industries, outlines the proposed AI-driven framework, and suggests practical implementation strategies for realizing improvements in MRR on horizontal boring machines.