This paper explores the application of machine learning (ML) methods to enhance decision-making and automation in gaming environments. It examines supervised, unsupervised, and reinforcement learning (RL) strategies, emphasizing RL’s effectiveness in interactive environments and its combination with deep learning (DL) to form deep reinforcement learning (DRL) approaches. Additionally, Game Theory (GT) is introduced as a framework for optimizing player interactions. By leveraging these concepts, several practical applications are presented across diverse gaming scenarios, from recreational games, such as Dota2 and ATARI, to serious games designed to educate about managing real-world resources and health complications cases, or replicate vehicle behaviour simulations from real-world driving scenarios. Findings show that DRL excels in complex decision-making tasks, while GT optimizes strategic interactions. Additionally, by considering recreational and serious games as case studies, this work aims to demonstrate the versatility of these methods, showing rich and dynamic environments for testing the adaptability and responsiveness, while can also offer a context for applying these advancements to simulate and solve real-world problems.