The field of computer science known as artificial intelligence, which enables the development of intelligent computers that can carry out tasks akin to those done by humans, is what we call the "age of artificial intelligence." Artificial Intelligence is applied in many fields, such as robotics, search engines, machine learning, and virtual personal assistants. A subfield of artificial intelligence known as computer learning has transformed conventional computing into a self-learning machine. The core idea of machine learning is the recurrent nature of analysing and drawing conclusions from data. These algorithms assess earlier calculations and forecast precise results using self-learning techniques. Massive data analysis is another area in which machine learning comes in quite handy. After gathering data, machine learning creates analytical models, trains users, and then launches the programme. ML algorithms are not only capable of learning from data, but they can also identify patterns to identify vulnerabilities and suggest fixes. Applications of machine learning can be found in many different areas, such as finance and healthcare. This work examines how neural networks use machine learning, how many hidden layers are used, and what activation value is used to build any biological neural network that uses machine learning to compute neurons. The fields of machine learning (ML) and artificial intelligence (AI) have bright futures.