Some of them include The Palmer Drought Severity Index (PDSI), The Standard Precipitation Index (SPI), The Effective Drought Index (EDI), Z-score, The Streamflow Drought Index (SDI), and The Standardized Precipitation-Evapotranspiration Index (SPEI) [22; 23; 24; 25; 26] that have received more attention than the others, owing to their simplicity of implementation and capacity to capture the spatiotemporal variability of droughts. In addition, droughts can also be monitored and detected with some innovative methods, such as The Satellite Gravimetry Technique [27]. Among these, the SPI stands out as a simple, objective, and widely accepted drought index which is used to monitor and assess drought. The SPI estimates the severity and duration of drought based on the deviation of precipitation from the long-term average [23]. The SPI is recommended by the World Meteorological Organization (WMO) as the preferable meteorological drought index. Its uncomplicated calculation, flexibility in terms of timescales, and minimal data requirements (only precipitation) distinguish it from the PDSI and SPI. The superiority of the SPI in the evaluation of drought has been demonstrated globally, including in South Africa, Finland, Turkiye, Iran, and Italy [28]. Therefore, the SPI is used for drought detection and projection and is part of monitoring systems such as the European Drought Center(EDC), the National Drought Mitigation Center, and the European Drought Observatory (EDO) [21]. In this study, the drought index values were also calculated using the SPI. Beyond detecting and monitoring drought, forward-looking predictions are significantly important for the sustainability of water resources. Researchers used data-based forecasting models to make future predictions. These models have managed to go beyond physical models, and their reliability has been emphasized by numerous studies [29]. Various researchers in the literature performed drought modeling analyses utilizing machine learning (ML) and deep learning (DL) techniques. The algorithm and model structure used in these studies may vary depending on the specific region being analysed. While Artificial Neural Networks (ANNs) may perform well in one region, Support Vector Machine (SVM) or Adaptive Neuro-Fuzzy Inference System ANFIS may perform effectively in another region. Furthermore, the findings can be altered or impacted by the parameters employed in the model structure or the number of lagged times considered for each parameter. In other words, the performance of the parameters used in model structure and ML algorithms used for one region may exhibit significantly different performance in another region. Nevertheless, the performance of widely used common algorithms does not change. What affects the result is the application of these algorithms with different optimization methods. Thus, by using the most suitable algorithm and optimization technique for a specific location or by employing strategies that enhance the model performance, one might accomplish diverse outcomes and improvements. These results emphasize the need to enhance our knowledge of droughs and improve prediction methods to develop effective strategies for dealing with the negative impacts of changing conditions. Investigating their emergence and evolution with a combination of conventional and more sophisticated metrics and methods is needed. To do so, ML and deep DL models are recognized as some of the most advanced tools for exploring climate system predictability and making more accurate predictions possible using observed climate data. ML techniques such as the ANFIS, ANN, Random Forest (RF), and Support Vector Machines (SVM) have been widely used in prediction models [30]. Machine learning methods are well suited for handling large amounts of data and complex analyses because they can efficiently model nonlinear and high-dimensional data that have intricate relationships and missing values.