<p>Terpenoids represent the largest and most structurally diverse class of secondary metabolites, with extensive applications in the pharmaceutical, nutraceutical, cosmetic, agricultural, fragrance, and biofuel industries. The growing demand for these compounds has resulted in extensive exploitation of plant-derived terpenoids, raising concerns regarding resource availability and sustainability. Consequently, microbial production has emerged as a promising alternative because of the high genetic tractability, rapid growth, and ease of metabolic engineering offered by microbial hosts. Various metabolic engineering strategies, including heterologous gene insertion, targeted gene deletion, and redirection of carbon flux from primary metabolism toward terpenoid biosynthesis, have been employed to enhance terpenoid production. The selection of an appropriate microbial host is a critical determinant of production efficiency, as it influences metabolite yield, cultivation feasibility, genetic manipulability, scalability, environmental sustainability, and economic viability. Genetically engineered microorganisms have therefore become well-established platforms for the production of diverse classes of terpenoids. Although substantial progress has been made in reconstructing and expressing terpenoid biosynthetic pathways in microbial hosts, further strain optimization requires systematic integration of computational approaches. In this context, artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools for metabolic engineering by enabling pathway prediction, metabolic flux optimization, enzyme engineering, and identification of bottlenecks throughout terpenoid biosynthesis. Coupled with advances in genomics, systems biology, and synthetic biology, these technologies are accelerating the development of robust microbial cell factories for the sustainable, large-scale production of terpenoids through industrial bioprocesses.</p>