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With the advancement of computer science, Artificial Intelligence (AI) is being incorporated into many fields to increase prediction performance. Disaster management is one of the main fields embracing the techniques of AI. It is essential to forecast the occurrence of disasters in advance to take the necessary mitigation steps and reduce damage to life and property. Therefore, many types of research are conducted to predict such events due to climate change in advance using hydrological, mathematical, and AI-based approaches. This paper presents a comparison of three major accepted AI-based approaches in flood and drought forecasting. In this study, fluvial floods are measured by the runoff change in rivers whereas meteorological droughts are measured using the Standard Precipitation Index (SPI). The performance of the Convolutional Neural Network (CNN), Long-Short Term Memory network (LSTM), and Wavelet decomposition functions combined with the Adaptive Neuro-Fuzzy Inference System (WANFIS) are compared in flood and drought forecasting, with five statistical performance criteria and accepted flood and drought indicators used for comparison, extending to two climatic regions: arid and tropical. The results suggest that the CNN performs best in flood forecasting with WANFIS for meteorological drought forecasting, regardless of the climate of the region under study. Besides, the results demonstrate the increased accuracy of the CNN in applications with multiple features in the input. [Display omitted] • Performances of three AI models were compared for flood and drought forecasting. • Comparisons were done for an arid and a tropical basins separately. • Statistical measures and event indicators were used to determine the best model. • WANFIS model showed the highest accuracy for drought forecasts in both regions. • CNN model was the best forecast regardless of the climate of the region. [ABSTRACT FROM AUTHOR]