Fog computing has emerged as a crucial support system for time-critical applications requiring real-time functionality, often executed on virtual machines (VMs) within fog nodes. Live VM migration, a prevalent technique in fog computing scenarios, involves the seamless transfer of VMs between nodes to ensure uninterrupted service delivery. However, existing models face challenges due to a simplistic decision-making process that often relies on a singular factor, such as mobility or load, without comprehensively considering multiple dynamic fog environment factors. One major challenge addressed by our proposed solution is the limited scope of decisionmaking factors in existing models. Traditional approaches predominantly focus on singular factors like mobility, load, or energy consumption for VM migration decisions. Notably, studies considering load often overlook the importance of a threshold value. In response, our proposed model adopts a holistic decision-making approach that integrates mobility and loa, incorporating thresholds. This approach aims to comprehensively address the latency requirements of real-time applications. The challenge of ensuring adaptability and intelligent decision-making within the dynamic fog environment is addressed through the integration of Reinforcement Learning (RL). RL imparts machine learning capabilities to the model, allowing it to adapt and optimize decisions based on evolving fog conditions. This approach enables our model to navigate the intricate fog environment by considering factors such as node characteristics, migration mechanisms, and existing proposals. To provide a more detailed breakdown, our live VM migration mechanism based on RL combines mobility and load factors with a load threshold for migration decisions. The incorporation of RL enables the model to dynamically adjust to varying fog conditions, ensuring efficient decision-making. Evaluated against models relying solely on mobility or load, VM_MIG consistently demonstrated lower total cost, surpassing mobility-only by 47.05% in high-mobility scenarios and outperforming load-only by 85% in high-load situations. VM_MIG exhibited stability and robustness, achieving an 83.8% improvement over mobility-based and 61.27% over load-only models at 1000 episodes. The combined effect of mobility and load factors showcased VM_MIG's superiority, highlighting the importance of considering multiple factors for migration decisions in fog environments. In addition, the model's ability to address the challenge of optimizing VM migration destinations in heterogeneous fog environments is noteworthy. By utilizing RL for decision-making, our model enhances adaptability and intelligence, specifically considering latency requirements and migration success rates for real-time applications. viii The comprehensive evaluation through the MATLAB 2022b simulator highlights significant performance improvements. The simulation results offer a thorough evaluation of the fog computing model, comparing EVM_MIG, Mob_MIG, and HM_MIG. EVM_MIG demonstrates scalability, outperforming HM_MIG across different fog node scales, with a 38.71% lower total delay at 64 nodes. Increased episodes showcase EVM_MIG's adaptability, achieving a 40.91% lower delay at 1000 episodes. Exploring fog node variations reveals EVM_MIG's efficiency, showing a 20.83% lower delay at 64 nodes. The investigation of virtual zones demonstrates a 50% reduction in total delay with 10 zones, highlighting zoning's impact. Sensitivity analysis underscores EVM_MIG's promising optimization, supported by numerical evidence, contributing valuable insights to fog computing advancements. To provide a more comprehensive breakdown, future enhancements could involve detailing specific challenges faced by the proposed models and precisely explaining how each aspect of the model addresses these challenges.