Detection of finger millet leaf diseases should be timely and accurate to ensure yield sustainability and food security. This paper deals with real-world inefficiencies, lack of data, low generalization, and lack of interpretability of the present disease prediction methods. This paper proposes a novel end-to-end machine learning pipeline by combining state-of-the-art techniques for increasing prediction accuracy, robustness, and transparency. The pipeline is using Cycle-Consistent Generative Adversarial Networks, or CycleGAN, to perform data augmentation, generating synthetic images that would appear real and represent various stages of disease under various environmental conditions. This enhances model generalization by 10–15%. The CNN-RF hybrid architecture was applied, which brought together the strengths of CNN’s spatial feature extraction with robust classification from Random Forest. With precision 0.90 and recall 0.85, accuracy was 88.5%. Based on the temporal environmental data, high-risk periods can be predicted with 87% accuracy by using LSTM networks for forecasting disease outbreaks. Further, feature-level interpretability based on SHAP gives better trust and transparency of predictions by the model. This piece of work offers a scalable and explainable solution to finger millet leaf disease detection with important implications for precision agriculture and sustainable disease management.