The synthesis of human walking is of great interest in biomechanics and biomimetic engineering due to its predictive ca- pabilities and potential applications in clinical biomechanics, rehabilitation engineering and biomimetic robotics. In this paper, the various methods that have been used to synthesize human walking are reviewed from an engineering viewpoint. This involves a wide spectrum of approaches, from simple passive walking theories to large-scale computational models integrating the nervous, muscular and skeletal systems. These methods are roughly categorized under four headings: models inspired by the concept of a CPG (Central Pattern Generator), methods based on the principles of control engineering, predictive gait simulation using optimisation, and models inspired by passive walking theory. The shortcomings and advantages of these methods are examined, and future directions are discussed in the context of providing insights into the neural control objectives driving gait and improving the stability of the predicted gaits. Future advancements are likely to be motivated by improved understanding of neural control strategies and the subtle complexities of the musculoskeletal system during human locomotion. It is only a matter of time before predictive gait models become a practical and valuable tool in clinical diagnosis, rehabilitation engineering and robotics. The synthesis of human walking is of great interest in biomechanics and biomimetic engineering due to its predictive ca- pabilities and potential applications in clinical biomechanics, rehabilitation engineering and biomimetic robotics. In this paper, the various methods that have been used to synthesize human walking are Review from an engineering viewpoint. This involves a wide spectrum of approaches, from simple passive walking theories to large-scale computational models integrating the nervous, muscular and skeletal systems. These methods are roughly categorized under four headings: models inspired by the concept of a CPG (Central Pattern Generator), methods based on the principles of control engineering, predictive gait simulation using optimization, and models inspired by passive walking theory. The shortcomings and advantages of these methods are examined, and future directions are discussed in the context of providing Insights into the neural control objectives driving gait and improving the stability of the predicted gaits. Future advances are likely to be motivated by improved understanding of neural control strategies and the subtle complexities of the musculoskeletal system during human locomotion. It is only a matter of time before predictive gait models become a practical and valuable tool in clinical diagnosis, rehabilitation engineering and robotics.