The study develops a machine learning-based framework of drought prediction and resilience for wheat cultivation that bypasses some weaknesses in traditional models reliant on outdated climate data and static statistical techniques. Currently used systems cannot adapt to changing environmental conditions and therefore lead to incorrect resource management. The proposed framework, using deep learning algorithms such as LSTM and Random Forest, provides real-time predictions and more localized insights using comprehensive datasets. Results show an increase in accuracy (89%) over existing systems (72% and 78%), along with improved precision (85%) and recall (90%). Prediction errors for all risk categories were significantly smaller, demonstrating the validity of the framework. User feedback reported high satisfaction with respect to usability and on-time alerts, asserting the advantage of the model in promoting sustainability and informed decision-making against climate variability. © 2025 Elsevier B.V., All rights reserved.