Retrosynthesis is a strategy to analyze the synthetic routes for target molecules in medicinal chemistry. However, traditional retrosynthesis predictions performed by chemists and rule-based expert systems struggle to adapt to the vast chemical space of real-world scenarios. Artificial intelligence (AI) has revolutionized retrosynthesis prediction in recent decades, significantly increasing the accuracy and diversity of predictions for target compounds. Single-step AI-driven retrosynthesis models can be generalized into three types based on their dependence on predefined reaction templates (template-based, semitemplate-based methods, template-free models), with respective advantages and limitations, and common challenges that limit their medicinal chemistry applications. Moreover, there are relatively inadequate multi-step retrosynthesis methods, which lack strong links with single-step methods. Herein, we review the recent advancements in AI applications for retrosynthesis prediction by summarizing related techniques and the landscape of current representative retrosynthesis models and propose feasible solutions to tackle existing problems and outline future directions in this field.