Predefined risk events associated with the behavior of people with ID are challenging to predict since risk behaviors can change depending on time and may be affected by various contextual, communicative, environmental, and supportive factors. Existing conventional techniques offer inadequate representation of changes over time and fail to give a satisfactory explanation for individual predictions. This paper suggests an adaptive Transformer-based explainable learning model for longitudinal risk prediction and interventions. A simulated longitudinal dataset of 300 individuals, 12 sequences per individual, 3,600 data points, and 18 predictors was generated for testing purposes. The predictors were related to history of behavior, behavior changes over time, contextual, supportive, and intervention factors. Individuals were split into training (70%), validation (15%), and testing (15%) groups. The proposed model includes temporal embedding, an adaptive Transformer encoder, adaptive temporal gating, symbolic knowledge about the problem domain, risk prediction, explainability, and rules-based interventions. The proposed model is compared with Logistic Regression, Random Forest, XGBoost, LSTM, and traditional Transformer using discrimination and calibration measures. In a controlled simulation experiment, the proposed framework attained an accuracy of 93.1%, F1-score of 89.0%, ROC-AUC of 0.962, and PR-AUC of 0.887, compared with 89.2%, 82.7%, 0.926, and 0.821, respectively, for the baseline Transformer framework... These benchmark results demonstrate the architectural viability of integrating adaptive temporal learning with symbolic reasoning; however, clinical utility and real-world efficacy remain subject to prospective validation on non-synthetic care data. The proposed framework is meant to be a decision-making tool rather than a deterministic classifier of individuals. Further validation through real-world longitudinal data and prospective professional assessment is necessary.