Predicting water availability in Southern Africa requires a multifaceted and integrated approach due to the region’s hydrological complexity and climatic variability. This study critically reviews key data sources such as satellite remote sensing, ground-based observations, and institutional databases, and evaluates their strengths, limitations, and integration potential for predictive modelling. Particular attention is given to the challenges of data heterogeneity, scarcity, and quality. To address these, a combination of data fusion, imputation, quality control, and open data frameworks is proposed. The review further explores ethical and security considerations associated with large-scale environmental datasets. Machine learning techniques such as support vector machines, random forests, and neural networks are examined alongside hydrological and statistical models to determine their suitability for forecasting water availability under various climate scenarios. The importance of incorporating validated climate change projections is underscored for long-term scenario planning. Ultimately, the study presents a methodological foundation for developing reliable, dynamic, and policy-relevant water availability forecasts. The insights generated are expected to support sustainable water resource management and climate resilience in Southern Africa.