<p>Massive Machine Type Communications (mMTC) are integral to the Internet of Things (IoT) ecosystem, requiring efficient and reliable access control mechanisms to manage the enormous number of connected devices. Traditional access control schemes such as Dynamic Resource Allocation (DRA), Slotted Aloha, and Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) face critical challenges in scalability, energy efficiency, and collision management, particularly in dense IoT networks. To address these limitations, this paper proposes a Hybrid Access Control (HAC) mechanism that dynamically combines the strengths of these approaches based on network conditions. A comprehensive performance evaluation of HAC is conducted, focusing on metrics such as average energy consumption, access delay, and collision rate. Simulations over 1000 iterations with varying device densities (100–1000 devices) reveal that HAC significantly reduces collision rates and access delays while enhancing energy efficiency compared to traditional schemes. The results are presented with error bars to illustrate variability and ensure the reliability of the findings. HAC’s ability to adapt dynamically to diverse network conditions positions it as a robust solution for mMTC applications, enabling reliable and energy-efficient communication in densely populated IoT networks. While the current study focuses on foundational metrics, future research will expand the evaluation to include additional metrics, and explore the integration of advanced machine learning techniques to enhance HAC’s adaptability and scalability. This work demonstrates the potential of HAC to advance the performance and scalability of mMTC, paving the way for broader IoT adoption.</p>