This research advances the integration of AI and robotics for autonomous exploration and navigation in dynamic and unstructured environments. Employing an interpretivist philosophy, a deductive approach, and a descriptive design, the study focuses on sensor fusion, SLAM techniques, decision-making algorithms, and platform integration. Through secondary data collection, technical details encompassing Bayesian sensor fusion, CNN-based perception, and Graph SLAM with loop closure detection are explored. Hardware modifications enable seamless AI-robotics integration, with software middleware like ROS facilitating real-time data processing. Rigorous testing and validation in simulated and real-world environments confirm system robustness. The research reveals that advanced perception strategies and SLAM techniques significantly enhance environmental understanding and mapping accuracy. Decisionmaking algorithms, particularly RL methods, demonstrate adaptability and intelligence in navigation. The integration of AI with the robotic platform showcases the pivotal role of hardware and software harmonization in achieving seamless operation. Recommendations include exploring semantic perception, dynamic obstacle avoidance, and multi-agent collaboration. Future work should focus on deep reinforcement learning, bio-inspired algorithms, and emerging sensor technologies.