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In this investigation, supervised machine learning (ML) was utilized to accurately predict the optimum bromine doping concentration in single-junction MASnI 3−x Br x devices. Data-driven optimizations were carried out on 42 000 unique devices built utilizing a solar cell capacitance simulator (SCAPS). The devices were investigated through variations of bromine doping %, bandgap, electron affinity, series resistance, back-contact metal, and acceptor concentrationparameters that were specifically chosen because of their tunable nature and ability to be modified through facile experimental fabrication techniques of the device. Five different algorithms were utilized to explore feature engineering. The first step before bromine doping within the device included validation studies of a pure tin-based system, MASnI 3 : a power conversion efficiency (PCE) of 6.71% was achieved, having close congruence with experimental data. ML analyses for optimal bromine doping resulted in the discovery of two devices with bromine concentrations of 22.43% (Br22) and 25.63% (Br25), with the latter being a more fine-tuned value obtained through extra rigorous analysis. To understand the total and relative impact of each feature on power conversion efficiency (PCE), Br22 and Br25 were analyzed with a state-of-the-art algorithm, namely, the SHapley Additive exPlanations (SHAP) algorithm. Focusing on the two discovered devices, further device optimizations were carried out utilizing SCAPS. Modulations of absorber thickness, bulk and interfacial defect density, and choice of electron transport layer (ETL) and hole transport layer (HTL) materials were tried. Device stability was analyzed through carrier lifetime studies. Following these optimization steps, Br22 and Br25 demonstrated final high PCE values of 20.72 and 17.37%, respectively. The ML-assisted quantitative analysis of the current work provides significant confidence for optimal bromine-doped tin-based devices to be considered as viable and competitive nontoxic alternatives to traditional technologies.