Early warning systems (EWS) have been increasingly used to mitigate crop failure risks in developing countries, especially in remote rural areas where traditional monitoring methods are insufficient. Mozambique is a case study of such regions with high vulnerability to climate-related disasters. The methodology involves collecting and preprocessing climate-related data from meteorological stations, integrating it with soil moisture, rainfall, and temperature sensors. A convolutional neural network (CNN) model is trained using historical crop yield data as labels for early warning prediction. A CNN model achieved an accuracy of 85% in predicting potential crop failure within the next three months, identifying areas at higher risk with a spatial distribution pattern across different climatic zones. The AI-powered EWS demonstrated promising results in reducing false positives and negatives through real-time monitoring and feedback loops to improve model performance over time. Future research should focus on integrating user feedback into the system for better decision-making, ensuring data privacy and security, and scaling up deployment across more rural areas of Mozambique. AI, Early Warning Systems, Climate Change, Crop Failure Prevention, Machine Learning Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.