Climate variability and shifting atmospheric patterns have emerged as critical determinants of vector-borne and waterborne disease incidence across tropical and subtropical regions. This paper presents a comprehensive machine learning- based early warning framework that integrates multi-source climate parameters—including temperature, relative humidity, rainfall intensity, wind velocity, and atmospheric pressure gradients—with epidemiological surveillance records to forecast disease outbreak risk at district and sub-district levels. The proposed system employs an ensemble architecture combining Random Forest, Gradient Boosting Machine (GBM), Long Short-Term Memory (LSTM) networks, and an Attention-Enhanced Convolutional Neural Network (ACNN) to capture both spatial heterogeneity and temporal dependencies inherent in climate- disease relationships. Training data spans seventeen years of merged meteorological observations sourced from the India Meteorological Department (IMD) and ISRO's MOSDAC satellite telemetry, cross-referenced with disease incidence records from the Integrated Disease Surveillance Programme (IDSP). Experimental results demonstrate an overall outbreak prediction accuracy of 93.7%, area under the receiver operating characteristic curve (AUC-ROC) of 0.96, and a mean absolute error of1.4 cases per 10,000 population for weekly incidence estimation. The framework incorporates an automated alert generation module that classifies health risk into four tiers—green, yellow, orange, and red—enabling district health officers to pre- position medical resources and initiate preventive interventions up to three weeks in advance of a projected epidemic threshold crossing. This work contributes a replicable, data-driven methodology applicable to resource-constrained public health systems in developing nations.