Artificial Intelligence (AI) has emerged as a vital tool for predicting crop performance, including crop growth, diseases, and yield, and assisting in informed decision-making, such as predicting precise water management practices for sustainable water use, thereby maximizing growers’ profits. Climate change and the increasing frequency of severe droughts have significantly affected crop yields, posing a challenge to global food security. Therefore, predicting crop performance, particularly expected yield, is crucial during water stress. AI algorithms, including Random Forest (RF), Gradient Boosting Machines (GBM), Artificial Neural Networks (ANNs), and Convolutional Neural Networks (CNNs), enable the integration of weather, soil, crop, and irrigation management data to predict crop performance under drought with higher accuracy. This review focuses on the applications of AI-based models in quantifying the impact of drought on crop performance, considering nonlinear soil–plant–atmosphere interactions and the integration of spatial data through remote sensing. However, data limitations, modeling dynamic drought responses, and addressing spatial heterogeneity in drought indices pose significant challenges for AI-based predictions. Case studies demonstrate AI's potential to predict crop yields under drought conditions, with applications ranging from traditional machine learning (ML) models to advanced deep learning (DL) frameworks. However, there is no clear consensus on the best AI model for drought-induced water stress management, although ANNs and RFs are more commonly used than others in precision-based irrigation and crop yield prediction. DL-based models, such as CNN and Long Short-Term Memory (LSTM), are less frequently applied, despite their strengths in handling complex and unstructured data, as well as modeling long-term drought impacts. Refining AI algorithms to support precision irrigation scheduling, integrating AI with conventional water management practices, and enabling data-driven decision support systems will be vital for mitigating the adverse effects of drought on crop performance.