The early detection and diagnosis of disease is critical to outpatient care improvements health outcomes reduces the cost. The extent of Recent Advances This field has witnessed significant progress by incorporating the pattern recognition methodologies with biomedical signal and image processing. This research work investigates the use of some pattern recognition algorithms for analysis of different biomedical data such as Electroencephalogram (EEG), Electrocardiogram (ECG) and Medical imaging like X-ray, Magnetic Resonance Imaging (MRI). This article mainly concentrates on developing efficient algorithms to capture relevant variables from the noisy and high-dimensional nature of medical data. After that, differentiating healthy state from disease in terms of these features is done. This research is predominantly handled using Machine Learning and Deep Learning methods. To focus on methods that are capable of dealing with the natural variability in biological data, age, race and individual differences. Creating strong algorithms means solving problems related to data quality, lack of training samples and understandability in a machine learning model.