Accurate drought prediction plays a pivotal role in water resource management and agricultural planning. This study delves into the realm of machine learning algorithms to enhance the accuracy of such predictions. The core focus centers on the integration of support vector neural networks (SVNNs) as a means to predict drought occurrences, leveraging data from diverse sources including meteorological, hydrological, and remote sensing. The unique characteristic of SVNNs lies in their amalgamation of support vector machines and neural networks, allowing for the nuanced capture of intricate data relationships. The refinement of feature engineering strategies is instrumental in optimizing the predictive models. These strategies address the challenges posed by imbalanced datasets through the implementation of resampling techniques, paired with careful selection of evaluation metrics. The performance evaluation of SVNNs is conducted through a rigorous assessment against historical drought events, further accentuated by a comparison against conventional methods. To expand the scope, the integration of remote sensing data enriches the models with comprehensive spatiotemporal insights, enhancing their predictive capabilities. Moreover, the research extends its exploration into ensemble techniques and hybrid models, showcasing the versatile potential of machine learning in the domain of drought prediction. The findings of this study unequivocally demonstrate the superiority of the projected method when compared to existing methodologies, particularly in terms of accuracy, precision, and F1-score. The novel drought prediction models put forward in this research hold the potential for unprecedented advancements, significantly enhancing the efficacy of drought mitigation strategies.