Falls in institutionalized populations represent a major clinical and operational challenge, motivating decision-support tools for risk stratification and outcome prediction. This study presents a machine learning pipeline to predict post-fall mortality and related fall outcomes in nursing home residents using routinely collected structured variables, including demographic, mobility, and functional assessment data. Data from 2009 to 2022 were organized into task-specific datasets comprising a falls-only cohort of 1,368 records and a mixed cohort of 1,445 cases. Preprocessing involved one-hot encoding, MinMax scaling, and stratified 80/20 splits. To address severe class imbalance in mortality prediction, autoencoder-based data augmentation was applied, followed by grid-search model selection and SHAP-based interpretability. Fall occurrence prediction achieved the highest performance, with Gradient Boosting reaching a Macro-F1 of 0.89. Post-fall mortality prediction remained challenging due to extreme imbalance, yielding a Macro-F1 of 0.48, while the multiclass fall-count severity task achieved a Macro-F1 of 0.46. These findings demonstrate the potential of explainable machine learning for fall-related risk stratification in nursing homes while emphasizing the need for larger datasets, richer clinical variables, and external validation to improve rare-event prediction and support real-world implementation in long-term care settings.