Understanding and managing charge carrier recombination dynamics is crucial for optimizing the performance of metal halide perovskite optoelectronic devices. In this work, we introduce a machine learning-assisted intensity-modulated two-photon photoluminescence microscopy approach for quantitatively mapping recombination processes in MAPbBr₃ perovskite microcrystalline films at micrometer-scale resolution. To enhance model accuracy, a balanced classification sampling strategy was applied during the machine learning optimization stage. The trained regression chain model accurately predicts key physical parameters—exciton generation rate (G), initial trap concentration (N_TR), and trap energy barrier (E_a)—across a 576-pixel spatial mapping. These parameters were then used to solve a system of coupled ordinary differential equations, yielding spatially resolved simulations of carrier populations and recombination behaviors at steady-state photoexcitation. The resulting maps reveal pronounced local variations in exciton, electron, hole, and trap populations, as well as photoluminescence and nonradiative losses. Correlation analysis identifies three distinct recombination regimes: 1) a trap-filling regime predominated by nonradiative recombination, 2) a crossover regime, and 3) a band-filling regime with significantly enhanced radiative efficiency. A critical trap density threshold (~10¹⁷ cm^-3) marks the transition between these regimes. This work demonstrates machine learning-assisted intensity-modulated two-photon photoluminescence microscopy as a powerful framework for diagnosing carrier dynamics and guiding defect passivation strategies in perovskite materials.