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Abstract The sub-Saharan Africa region continues to experience food insecurity, a consequence of the less productive agricultural sector that has dragged to adapt to the effects of climate change. As the region’s population continues to grow, there is a need to modernize the region’s agricultural sector to meet the increasing food demand. Although extreme atmospheric conditions cannot be entirely mitigated, however, the integration of technologies such as the Internet of things (IoT) and machine learning (ML) can increase the quantity and quality of production from the crop fields. These technologies have the potential to empower agricultural management systems to handle both climatic and farm data in an orchestrated manner, and inform the formulation of effective strategies. This study presents the design and development of a system for predicting crop yields that integrates IoT and ML. The system combines historic and current weather and crop yield data to predict seasonal crop yields. The weather parameters including, rainfall, temperature, humidity and soil moisture are collected by IoT sensors and transmitted to the cloud for crop yield forecasting. The system is used to analyze seasonal yields of Irish-Potato and Maize in Musanze District of Rwanda. Using data over different agricultural seasons, the system achieved favorable predictive accuracy with mean absolute percentage error (MAPE) values of 0.339, 0.309, and 0.177 for two seasons of Irish potatoes and one season of maize, respectively. Such predictive yield systems can reduce food insecurity risks and enhance harvest efficiency by enabling early awareness of crop production, fostering effective strategies shared among decision-makers and stakeholders. While maize and Irish potatoes were the initial case studies, expansion to include other crops and more variables is envisioned.