Accurately predicting disease risk requires advanced modelling techniques capable of handling dynamic environmental and personal health data. This research introduces a hybrid Recurrent Neural Network (RNN) and Support Vector Machine (SVM) framework for predicting asthma attacks and Type-2 diabetes risk. For asthma, an RNN processes sequential real-time environmental inputs-such as air quality, temperature, and humidity-capturing temporal dependencies to forecast attack likelihood. The RNN's ability to learn from time-series data allows it to adapt to changing conditions, improving prediction accuracy over time. Meanwhile, SVMs assess Type-2 diabetes risk by classifying personalized health factors, including medical history, lifestyle habits, and biomarker trends, while also analyzing environmental risk groups through statistical odds ratios. Developed using TensorFlow, PyTorch, and Scikit-learn, this cloud-based system dynamically refines its models with new data, enabling early intervention and reducing healthcare burdens. Experimental results demonstrate that the RNN-SVM hybrid approach effectively combines temporal pattern recognition with high-precision classification, offering a robust solution for proactive disease risk management.