<p>Model checking is crucial in statistical analyses and has garnered significant attention in the academic literature. However, certain challenges persist in scenarios that involve large-scale datasets and limited resource allocations. This research introduces a novel subsampling methodology for testing regression models with continuous and categorical predictors, referred to as the Subsampling Adaptive Projection-Test (SAPT). This innovative approach demonstrates substantial improvements in test power for both local and global alternatives, outperforming conventional uniform subsampling mechanisms. The authors rigorously establish the asymptotic properties of SAPT and delineate its maximum achievable power under asymptotic conditions. Comprehensive simulations and real-world dataset applications provide robust validation of the proposed theoretical propositions.</p>