Muscle synergy analysis has been widely applied in neuroscience, biomechanics, rehabilitation, and robotics. However, the reliability of muscle synergy extraction remains a critical issue, as results are highly sensitive to variations in experimental design, surface electromyography preprocessing, factorization algorithms, and the determination of the number of synergies. This narrative methodological review examines the key factors that influence the robustness and reliability of muscle synergies within the framework of limb neuromechanical controllers across anatomical regions and task contexts. By analyzing the current state of research in this field, we first summarize the conceptual and methodological foundations of muscle synergy analysis and then critically examine the factors that influence its robustness and reliability. This review provides practical recommendations to enhance methodological rigor, including greater methodological harmonization and transparent reporting of experimental protocols, the use of musculoskeletal simulation to evaluate muscle selection strategies, and the refinement of synergy extraction techniques. These insights aim to improve the reliability and robustness of muscle synergy analysis and promote its application in robotics, human–machine interfacing, and computational motor control.