Efficient area coverage is a fundamental challenge in applications such as environmental monitoring, surveillance, and wireless sensor networks. This study employs stochastic modeling, specifically Poisson’s point processes and Voronoi tessellations, to optimize area coverage in dynamic and resource-constrained environments. By simulating autonomous agents such as sensors and robots using probabilistic decision-making and random walk strategies, the model evaluates different deployment approaches, including random and deterministic placements, under varying environmental conditions. MATLAB and Simulink simulations analyze key performance metrics such as coverage efficiency, energy consumption, and operational time. The results demonstrate that stochastic modeling enhances coverage efficiency by adapting to environmental uncertainties while minimizing resource utilization. Additionally, the approach facilitates obstacle avoidance, agent collaboration, and energy conservation, making it a robust framework for improving area coverage in unpredictable scenarios.