As the Internet of Medical Things (IoMT) expands, securing healthcare data has become increasingly vital due to the inherent vulnerabilities associated with wireless communication. Traditional security measures often fall short in effectively mitigating sophisticated cyber threats that target IoMT networks. This study proposes a Deep Reinforcement Learning (DRL)-based Intrusion Detection System (IDS) specifically designed to enhance transmission-level security for IoMT applications. The proposed system utilizes an advanced Deep Q-learning algorithm to develop optimal strategies for dynamic decision-making, allowing for real-time adaptation to emerging threats. Through comprehensive experiments and comparative analyses across IoMT datasets, the proposed DRL-based IDS demonstrates significant improvements in detection accuracy, precision, recall, and response time when compared to conventional IDS methodologies. The findings highlight the potential of employing DRL techniques to substantially strengthen the security posture of IoMT ecosystems, thereby ensuring the integrity and safety of sensitive healthcare data.