Cloud storage, processing, and analysis of health data are, therefore, among the very central considerations of this study toward improving health decisions via data management. It develops the cloud framework for health data analysis and classification to increase predictive accuracy. Data collection begins with cloud platforms and develops toward storage of increasingly flexible, scalable databases for health. The data comes into pre-processing or cleaning and normalization steps for analysis; a Linear Discriminant Analysis (LDA) feature is extracted from the data while maintaining the attention of class-discriminative information. A genetic algorithm in feature selection conducts feature relevance identification to improve performance. Finally, classification has been done using TabNet for better classification. This research study emphasizes a healthcare predictive model that claims high accuracy (98.7%), precision (98.6%), recall (99.1%), and F1 score (98.6). Throughput also increases with request rate values, but stabilizes at 0.5 req/s after 1.0 req/s, indicating diminishing returns, and thus is scalable and reliable with more comprehensive insights about health data and optimized predictions about outcomes of a patient so further decisions may be based on this information in patient healthcare.