Prosthetics and orthotics research, studies, and technologies have been evolving through the years. According to World Health Organization (WHO) data, it is estimated that, globally, 35–40 million people require prosthetics and orthotics usage in daily life. Prosthetics and orthotics demand is increasing due to certain factors. One of the factors is vascular-related disease, which leads to amputation. Prosthetic usage can increase an amputee’s quality of life. Therefore, studies of the ergonomic design of prosthetics are important. The ergonomic factor in design delivers prosthetic products that are comfortable for daily use. One way to incorporate the ergonomic design of prosthetics is by studying the human walking gait. This paper presents a multiclassification of human walking gait based on electromyography (EMG) signals using a machine learning method. An EMG sensor was attached to the bicep femoris longus and gastrocnemius lateral head to acquire the EMG signal. The experiment was conducted by volunteers during normal walking activity at various speeds and the movements were segmented as initial contact, which was labeled as initial gait; loading response to the terminal stance, which was labeled as mid-gait; and pre-swing to terminal swing, which was labeled as final gait. The EMG signal was then characterized using an artificial neural network (ANN) and compared to six training accuracy methods, i.e., the Levenberg–Marquardt backpropagation training algorithm, quasi-Newton training method, Bayesian regulation backpropagation training method, gradient descent backpropagation, gradient descent with adaptive learning rate backpropagation, and one-step secant backpropagation. The machine learning study performed well in the classification of three classes of human walking gait with an overall accuracy (training, testing, and validation) of 96% for Levenberg–Marquardt backpropagation. The gait data will be used to explore the design of lower limb prosthetics in future research.