<p>Mobile Edge Computing (MEC) is revolutionizing computing efficiency by shifting resource-intensive and latency-sensitive operations from mobile devices with limited resources to proximal edge servers. The implementation of real-time applications is considerably improved by this prototype since it helps in reducing latency and maximizing processing effectiveness. In order to enhance Mobile Edge Computing’s potential for task offloading, this article suggests a new framework that uses Q-learning, a model-free reinforcement learning method, to dynamically handle task offloading and resource allocation in a Mobile Edge Computing environment that fluctuates. The Q-learning framework provides state-conscious decision-making to maximize long-term rewards by learning optimal techniques for workload balance and task offloading through iterative interaction with the system. The system can work out complex trade-offs between latency and resource utilization due to its flexibility. As compared to conventional techniques, this system provides context-driven accumens by apprehending the temporal and spatial dynamics of MEC environments. The proposed method is well illustrated, and a sufficient number of simulations or iterations have been made, which makes the proposed framework perform better than traditional methods. In this, we have taken parameters such as energy consumption, task execution time, and system responsiveness for our simulation. Thereby, our approach framework makes it a climbable and effective choice for approaching MEC applications. Empirical results show that Q-Learning reduces latency and improves accuracy. With the lowest latency (85 ms), Q-Learning outperforms by dynamically optimizing task offloading in response to current circumstances like network state and resource availability.</p>