In the Engineering, Procurement, and Construction (EPC) sector, accurate cost estimations during the tendering phase are crucial for maintaining competitiveness, especially with constrained project schedules and rising labor expenses. Typically, these estimations are labor-intensive, relying heavily on manual evaluations of engineering drawings, which are often shared in PDF format due to intellectual property concerns. This study introduces an innovative solution tailored for the energy industry, utilizing Artificial Intelligence (AI) - primarily deep learning (DL) and machine learning (ML) techniques - to streamline material quantity estimation, thereby saving engineering time and costs. Built on empirical data from a large EPC company operating in the energy sector, AI-based product development experiences, and academic research, our approach aims to enhance the efficiency and accuracy of engineering work, promoting better decision-making and resource distribution. While our focus is on enhancing a particular activity within the case company using AI, the method's broader applicability in the EPC sector potentially benefits both industry professionals and researchers. This study not only advances a practical application but also provides valuable insights for those seeking to develop AI -driven solutions across various engineering disciplines.