<p>The growing demand for efficient search and rescue (SAR) operations in response to natural and man-made disasters has spurred the development of advanced control frameworks for unmanned aerial vehicles (UAVs). This paper introduces an enhanced density-driven control (<i>D</i>²<i>C</i>) framework that incorporates optimal transport (OT) theory to guide multi-agent UAV systems in non-uniform area coverage. By integrating sensing range constraints, this approach improves operational efficacy compared to traditional <i>D</i>²<i>C</i>, which often overlooks these factors and can lead to inefficiencies. We propose three different methods that optimize coverage and victim detection while considering sensor range constraints, evaluating their performance through simulations with varying domain sizes, agent starting positions, region densities, and victim percentages. Results show that these methods enhance target detection rates by focusing search efforts on high-priority areas, with specific approaches excelling under particular constraints. These findings provide strategic insights for optimizing UAV-based SAR operations and similar applications in complex environments.</p>