Accurate forecasting of medication usage and ICD-9/10 code streams is critical for optimizing medical logistics, especially during periods of high demand, such as pandemics, disease outbreaks, wartime, or natural disasters. In this study, we develop a novel and robust forecasting framework using unsupervised learning techniques and Natural Language Processing (NLP) methods to build vector representations of daily ICD-9/10 codes and medication daily usage from Electronic Health Record (EHR) data. Multiple forecasting models, including Linear Drift Model, Vector Autoregression (VAR), Temporal Fusion Transformer (TFT), and Autoregressive Long Short-Term Memory (AR-LSTM) are trained, tested and evaluated. Finally multiple TFT and AR-LSTM models with different lookback horizon are trained and ensembled together to achieve better forecasting accuracy in near further (10 days). The AI framework is validated using MIMIC-IV ER and MIMIC-III datasets, resulting in the average forecasting error 5.2% at 5-th day and 18.1% at the 10-th day. The results demonstrate the ensemble model’s superior performance on near-future medication usage forecasting and ICD code progression, offering valuable insights for healthcare logistics and decision making. The framework also provides the mechanism to detect the model drift and finetune the model if necessary, which offers a robust tool for managing healthcare logistics under extreme and fluctuating conditions.