Falls are a major public health problem, with serious implications for the functionality and quality of life of adults. Although various machine learning approaches have been proposed for predicting fall risk, most are based on uniform models for heterogeneous populations, ignoring the substantial differences between disease categories. This study proposes a machine learning framework based on subgroup analysis to predict the risk of falls in different disease categories using data from the UK Biobank. Participants were grouped into clinically relevant disease categories, and multiple machine learning models were developed and evaluated for each subgroup. Model performance was evaluated using accuracy, sensitivity, specificity, ROC-AUC, and F1-score, while the calibration of probabilistic predictions was examined using the Brier score. In addition, explainable artificial intelligence techniques were applied through SHAP to interpret predictions. These results indicate that the performance of the models differs greatly across the disease types, resulting in moderate to high values of ROC-AUC, up to a maximum of 0.95 in some subgroups. Overall health was identified as the most important factor in most subgroups, whereas the importance of the activity factor was higher in subgroups of hematological diseases. In summary, these findings highlight the potential of subgroup-based, interpretable machine learning models to support more personalized and clinically actionable fall risk assessment in populations with chronic diseases.