This review article provides a comprehensive survey of artificial neural network (ANN) applications in various real-world scenarios. It offers a taxonomy of ANNs and highlights current and emerging trends in ANN research, focusing on areas of focus for future studies. The study also addresses the challenges, contributions, performance comparisons, and critiques of ANN methods. ANNs are discussed in the context of their applications in computing, science, engineering, medicine, environmental science, agriculture, mining, technology, climate, business, arts, and nanotechnology. The article emphasizes the superior performance of feedforward and feedback propagation ANNs in solving human problems, based on factors such as accuracy, processing speed, latency, fault tolerance, volume, scalability, and convergence. The authors recommend combining multiple ANN models into a single network-wide application for future research. The review concludes by suggesting areas for improvement and future research directions, emphasizing the need for systematic approaches to enhance ANN development and performance.This review article provides a comprehensive survey of artificial neural network (ANN) applications in various real-world scenarios. It offers a taxonomy of ANNs and highlights current and emerging trends in ANN research, focusing on areas of focus for future studies. The study also addresses the challenges, contributions, performance comparisons, and critiques of ANN methods. ANNs are discussed in the context of their applications in computing, science, engineering, medicine, environmental science, agriculture, mining, technology, climate, business, arts, and nanotechnology. The article emphasizes the superior performance of feedforward and feedback propagation ANNs in solving human problems, based on factors such as accuracy, processing speed, latency, fault tolerance, volume, scalability, and convergence. The authors recommend combining multiple ANN models into a single network-wide application for future research. The review concludes by suggesting areas for improvement and future research directions, emphasizing the need for systematic approaches to enhance ANN development and performance.