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The global burden of diabetes mellitus (DM) continues to escalate, posing significant challenges to healthcare systems worldwide. This study compares machine learning (ML) and deep learning (DL) methods and their ensembles for predicting the health outcomes of diabetic patients. This work aims to find the best solutions that strike a compromise between computational economy and good prediction accuracy. The study systematically assessed a range of predictive models, including sophisticated DL techniques and conventional ML algorithms, based on computational efficiency and performance indicators. The study assessed prediction accuracy, processing speed, scalability and resource consumption, and interpretability using publicly accessible diabetes datasets. It methodically evaluates the selected models using key performance indicators (KPIs), training times, and memory usage. DT achieved the highest F1-score of 0.98, indicating excellent overall performance in balancing precision and recall. However, the RF model demonstrated higher accuracy on the hospital dataset. The results highlight how lightweight, interpretable ML models work in resource-constrained environments and for real-time health analytics. The study also compares its results with existing prediction models, confirming the benefits of selected ML approaches in enhancing diabetes-related medical outcomes. This study is substantial for practical implementation, providing a reliable and efficient framework for automated diabetes prediction to support proactive disease management techniques and tailored treatment.