Climate change and economic instability are major threats to food security around the world, especially in developing countries where farming is still the main source of income. This paper introduces a new integrated forecasting system that uses real-time climate data, economic indicators, and agricultural metrics to improve resilience and food security. In Kenya’s maize-growing areas from 2021 to 2024, the system showed a 31% drop in crop losses due to bad weather, a 42% rise in the stability of farmer income, and a 58% rise in the accuracy of early warning systems. The framework uses data from 847 weather stations, 12,400 IoT soil sensors, and several economic databases to make forecasts that can be used right away, from daily to seasonal. Validation against historical drought conditions demonstrates that the system can produce timely and actionable early-warning signals, with modeled analyses indicating significant potential for loss mitigation under unfavorable climate scenarios. The results show that integrated climate economic forecasting systems can be used as decision-support tools for climate-smart agriculture. The results also demonstrate interrelated impacts across climate, economic, and agricultural dimensions.