Chronic Kidney Disease (CKD) is a global silent epidemic affecting 10% of the population, with its asymptomatic nature and fragmented medical records often delaying diagnosis until irreversible stages. Traditional screening relies on static lab tests, failing to capture dynamic physiological shifts. This study introduces a Federated Multimodal AI Framework to democratize renal diagnostics via a privacy-preserving, decentralized architecture. The motivation is to shift from reactive care to proactive, continuous monitoring in resource-constrained rural areas. By utilizing Federated Learning, the system trains robust models across healthcare nodes without transferring sensitive raw data, ensuring strict privacy compliance. Its core contribution lies in integrating clinical records, wearable sensors, and renal imaging, providing a scalable solution for early detection and progression analysis while overcoming the systemic bottlenecks of diagnostic latency and data siloing. The objective is to fuse diverse data streams including clinical laboratory records, longitudinal vitals from wearable sensors such as heart rate and blood pressure, and structural renal ultrasound imaging—into a single predictive engine. The methodology involves advanced pre-processing steps, such as K-Nearest Neighbour imputation for handling missing clinical values and sliding-window segmentation for temporal vitals. To process this data, a hybrid deep learning architecture is implemented: Convolutional Neural Networks (CNNs) extract structural features from kidney scans to detect physical scarring, while Long Short-Term Memory (LSTM) networks identify temporal patterns in comorbid vitals. An attention-based fusion mechanism then weighs these inputs, and model transparency is ensured through SHAP analysis, which provides clinicians with clear, biomarker-driven justifications for every risk assessment. Experimental evaluation of a new framework across 13,900 records demonstrated superior performance, achieving an F1-score of 0.931 and AUC-ROC of 0.952, with high accuracy (92.7%–96.2%) across disease stages. A 500-patient pilot study in Neyyattinkara showed significant clinical impact, including a 68% reduction in diagnostic costs, a drop in delays from 127 to 12 days, and a 35% dialysis deferral rate. Future scope for this research includes integrating multi-omics and genomic data to enable personalized precision medicine, as well as the creation of Longitudinal Digital Twins to simulate disease trajectories. By deploying these models via Edge AI and implementing Secure Multi-Party Computation, the framework will continue to evolve as a scalable, transparent, and highly secure tool for global renal health.

 Keywords: Federated Learning, Chronic Kidney Disease, Convolutional Neural Network, Long Short-Term Memory, Patient Health Monitoring and Multimodal Medical Data Analysis.