Retrosynthesis has come a long way in employing various artificial intelligence (AI) methodologies, notably revising how synthetic routes are designed and evaluated. Among common AI-driven retrosynthesis techniques, single-step and multistep approaches utilize different algorithms such as breadth-first search, beam search, and Monte Carlo tree search, all optimizing predictive performance for complicated synthesis processes. This chapter highlights the various AI-based techniques for retrosynthetic analysis by their effort-saving, cost-saving, and accessible means for improved overall feasibility of synthetic routes. An important point of these techniques is scoring functions and datasets that serve as useful instruments for evaluating the proposed routes’ synthetic efficiency, economic viability, and thermodynamic feasibility. Several examples from the pharmaceutical and biocatalysis fields demonstrate how AI will enable better drug design and biotechnological processes. By leveraging comprehensive chemical databases such as the USPTO reaction dataset, Reaxys, and Pistachio, AI systems can tap into vast amounts of reaction data, refining their predictive models. The chapter concludes on a note underlining the enormous promise of AI-retrosynthetic planning for the future of synthetic chemistry by allowing drug discovery and biotechnology innovations to become cheaper, less wasteful, and more efficient methods.