One of the greatest challenges in the design of realistic Artificial Intelligence (Al) in computer games is agent movement. Pathfinding strategies are usually employed as the core of any AI movement system. The two main components for basic real-time pathfinding are (i) travelling towards a specified goal and (ii) avoiding dynamic and static obstacles that may litter the path to this goal. The focus of this paper is how machine learning techniques, such as Artificial Neural Networks and Genetic Algorithms, can be used to enhance an AI agent's ability to handle pathfinding in real-time by giving them an awareness of the virtual world around them through sensors. Thus the agents should be able to react in real-time to any dynamic changes that may occur in the game.