Artificial intelligence (AI) is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality. The distinction between the former and the latter categories is often reveal. AI is usually labelled as artificial general intelligence while attempts to emulate natural intelligence have been called artificial biological intelligence. The term artificial intelligence is often used to describe machines that cognitive functions that humans associate with the human mind such as learning and problem solving . "Artificial intelligence is a technology using which we can create intelligent systems that can simulate human intelligence ". Machine learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data.It can easily make predictions or decisions without being explicitly programmed to do. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks. "Machine learning is a subfield of artificial intelligence, which enables machines to learn from past data or experiences without being explicitly programmed". Lately, Artificial Intelligence and Machine Learning is a hot topic in the tech industry. Perhaps more than our daily lives AI is impacting the business world more. There was about more than 300 million in venture capital invested in AI startups in 2014, a 300% increase than a year before .Artificial intelligence is the ability of a machine to perform cognitive tasks to achieve a particular goal based on provided data is revolutionizing and reshaping our health-care systems. The current availability of ever-increasing computational power, highly developed pattern recognition algorithms and advanced image processing software working at very high speeds has led to the emergence of computer systems that are trained to perform complex tasks in bioinformatics, medical imaging and medical robotics. Computer-based decision-support systems based on machine learning have the potential to revolutionize medicine by performing complex tasks that are currently assigned to specialists to improve diagnostic accuracy, increase efficiency of throughputs, improve clinical workflow, decrease human resource costs and improve treatment choices. These characteristics could be especially helpful in the management of prostate cancer, with growing applications in diagnostic imaging, surgical interventions, skills training and assessment, digital pathology and genomics. Medicine must adapt to this changing world, and urologists, oncologists, radiologists and pathologists, as high-volume users of imaging and pathology, need to understand this burgeoning science and acknowledge that the development of highly accurate AI-based decision-support applications of ML will require collaboration between data scientists, computer researchers and engineers.ML algorithms are able to enhance prostate cancer treatment by augmenting the surgeon's display with information such as cancer localization during robotic procedures and other image-guided interventions and could be used towards autonomous manipulation of tools for assistance in the operating room.