Simultaneous Localization and Mapping (SLAM) technologies are pivotal in advancing robotics and autonomous navigation, particularly within challenging indoor environments. These systems face significant challenges due to dynamic variables such as fluctuating lighting, occlusions, and the presence of moving objects. In response, this paper introduces a novel integration of Deep Reinforcement Learning (DRL) with the advanced object detection capabilities of YOLOv8 within a SLAM framework, termed DRL-SLAM YOLOv8. This integration enhances object detection by leveraging DRL’s ability to learn from environmental interactions and YOLOv8’s precise and efficient real-time object recognition. Our experiments demonstrate the superiority of DRL-SLAM YOLOv8 over traditional methods, with marked improvements in detection accuracy and system reliability under diverse conditions. Notably, our approach significantly improves navigational effectiveness, as evidenced by increased distance coverage and goal achievement compared to standard SLAM techniques, validating its potential in real-world applications.