This paper presents a study of machine learning techniques for medical and biomedical signal analysis. Measurements of physiological processes that reveal the state of health of living things are known as medical and biomedical signals. These signals are difficult to analyse using traditional signal processing techniques because they are frequently influenced by noise, non-stationarity, complexity, and high dimensionality. Algorithms that use machine learning may learn from data and provide hypotheses or predictions based on the patterns they spot. This paper examines several common electrocardiograms and electroen-cephalograms, among other biological and medical signals, and demonstrates how machine learning techniques may be used to analyse them for a variety of purposes, including diagnosis, treatment, monitoring, and research.