In computer vision applications including object recognition, surface defect detection, pattern recognition, medical picture analysis, etc., texture analysis is crucial. The spatial organisation of pixel intensities in a picture that repeats frequently over the entire image or in specific sections is referred to as the texture. The primary phrase used to describe the concepts or things in an image is its texture. Since then, numerous strategies have been put forth to adequately represent texture images. The four main categories of texture analysis techniques are statistical, structural, model-based, and transform-based techniques. The human visual system primarily uses texture, colour, and shape to identify the contents of images. First and foremost, effective and updated texture analysis operators are described in depth in this study. Next, some cutting-edge techniques for using texture analysis in medical applications and illness diagnostics are presented. In terms of accuracy, dataset, applicability, etc., various methodologies are contrasted. The effectiveness of discriminating, computing complexity, and resilience to difficulties like noise, rotation, etc. are the main considerations in all of the approaches that have survived. Results show that texture features, either alone or in combination with other feature sets like depth, colour, or shape features, provide high accuracy in classifying medical images.