Respiratory disorders continue to pose a significant global health concern, necessitating prompt, precise, and scalable solutions for diagnosis and monitoring. Traditional respiratory evaluations frequently rely on face-to-face examinations, restricting accessibility and the continuity of care. This work tackles the research issue of attaining continuous, distant, and precise prediction of pulmonary conditions through the use of multimodal AI combined with IoT-based e-Health systems. The main goal was to create and evaluate the Respiratory Analysis and Prediction Tool (RAPT), a system that integrates audio data and patient metadata to categorise respiratory disorders. Employing a dual-branch convolutional neural network trained on mel spectrograms and standardised clinical data (age, sex, BMI), RAPT attained a validation accuracy of 77.08% on a subset of the Respiratory Sound Database. The Gradio-powered interface facilitates real-time inference, and the tool is engineered for compatibility with IoT-enabled smart stethoscopes and wearable devices. Principal findings underscore robust efficacy in classifying COPD (precision 0.81, recall 1.00), however class imbalance constrained accuracy for less prevalent conditions such as Bronchiectasis. The paper finds that RAPT shows potential viability for personalised and remote respiratory monitoring but necessitates enhancements via bigger balanced datasets, calibration of wearable sensors, secure edge-based processing, and interoperability with 5G and Zigbee networks. This research establishes a basis for enhancing AI-driven, IoT-integrated e-Health platforms to transform respiratory health monitoring, facilitating continuous, accessible, and scalable patient care.