Cloud-based healthcare systems increasingly rely on predictive analytics to ensure service continuity and efficient resource utilization. However, traditional methods often fall short in delivering accurate maintenance predictions and demand forecasts, especially under dynamic and time-sensitive conditions. Conventional approaches like ACO-LSTM suffer from high latency, limited forecasting precision, and suboptimal performance in handling class imbalance, hindering their application in clinical environments. To address these limitations, the method proposes an AI-driven framework integrating lightweight deep learning for predictive maintenance and demand forecasting within cloud healthcare infrastructure. Unlike previous models, the approach combines predictive efficiency with low computational cost and responsiveness, making it well-suited for critical healthcare scenarios. Experimental evaluations demonstrate the superiority of the proposed method, achieving a prediction accuracy of 97.8%, an F1-score of 96.1%, and an MAPE of just 3.12%. In terms of cloud performance, the framework records a reduced latency of 41 ms and computation time of 41 seconds, outperforming the ACO-LSTM model across all metrics. Compared to ACO-LSTM’s 94% accuracy, 92.4% F1-score, and 5.47% MAPE, the model delivers consistent and reliable predictions with lower forecasting error and faster response. This advancement significantly enhances operational reliability and decision-making in healthcare systems. Overall, the proposed method offers a transformative leap toward intelligent healthcare infrastructure management, ensuring proactive service delivery and optimal resource planning.