ABSTRACT Human locomotion is an important activity of daily life. Every individual human walk with a specific gait pattern, and there underlay a neural command mechanism that works in synergy with that particular walking pattern. The presented study explored the neural command mechanism for walking at different speeds using muscle synergies and proposed a walking speed prediction algorithm. The difference in the neural control mechanism has been examined by comparing muscle synergies extracted for different walking speeds. The linear factorization technique was implemented to extract synergies from four lower limb muscle’s electromyogram signals, recorded at different walking speeds (3–11 km/hr). A 2- synergy model was found most suitable for all walking conditions; each synergy served a particular purpose during a complete gait cycle. The activation pattern of muscle synergy has shown a significant difference in terms of duration, timing, and magnitude (p-value < 0.05). Further, the same synergy model has been utilized for walking speed prediction, and an average accuracy of 97.28 ± 0.43% has been achieved. The performance of the developed prediction module has also been estimated over the lower range of walking speed (0.5–2.5 km/hr). The time complexity of the proposed algorithm has been found within the boundaries of its real-time implementation. The outcomes of the research work will be helpful for lower limb assistive/rehabilitative device control and the development of an effective human-machine interface.