Drought, a naturally occurring phenomenon, brings adverse impacts to both the environment and the agricultural sector. It is brought on by several variables, such as high temperatures, limited precipitation, and poor management of water resources. Recent developments in machine learning (ML) have opened new avenues for addressing the difficulties associated with monitoring and assessing droughts. This research employs smartphone images to forecast agricultural losses due to drought in Africa using advanced deep learning and transfer learning methodologies. The study makes three contributions: It improves the accuracy of drought damage prediction, applies a deep learning model to manage agricultural risk by evaluating insurance claims, and advances artificial intelligence to automate claims settlement. The study predicts the extent of crop damage due to drought using pre-trained neural network design and ResNet50, showing encouraging findings for potential future applications in drought-prone areas. However, the scope of this study is limited to exploring only one model. The potential of machine learning (ML) to enhance drought management and agricultural risk assessment is highlighted through a discussion of the research methodology, experimental results, limits, and future approaches.