In this paper, an intelligent advanced AI for the ancient puzzle game called Minesweeper will be presented in which some CSP methodologies will be integrated into the game to enhance the gameplay both in terms of accuracy and speed. For an example like Minesweeper, the user or player will probe a grid with safe cells revealed with knowledge about never touching or trying to peek into hidden cells which contain mines by knowing only numerical hints in the cells already discovered. It has modular game logic architecture, AI agent, and performance tracking so that decision-making and analysis could take place in real time. The three approaches would be used by the AI: all of these will have step-by-step algorithms, but the principal algorithm of CSP solving keeps at its center the activity of deducing safe moves and finding mines. Performance evaluations done by the authors indicate a significant learning curve, with AI efficiency improving through a higher win rate and better efficiency metrics across successive games. In fact, the results indicate improvement if the AI used logical deducing strategies with a significant difference. Generally, in addition to that balance evidence, it also demonstrates within the scheme of gameplay those strategies play together. The AI agent learned rapidly and peaked early in win rates, stabilized at around 80–90%. With time, efficiency improved, as completion time and move count reduced, which indicates optimization. Strategic analysis revealed a balanced approach of using constraint satisfaction, safe moves, and sometimes randomness. Decision times were effectively zero, indicating very fast processing, though timing resolution could be refined. The AI agent correctly changed its strategy, and high winning rates improved it. Further work in deductive safety of moves and resolution of timing will enhance its performance.