Drought is considered one of the detrimental effects of global warming that leads to severe environmental, social, and economic impacts. Multiple indices are used for the monitoring and assessment of drought throughout the world. Previous studies have been conducted in the literature to establish advanced drought monitoring methods and indices. The present study aimed at developing new meteorological drought indices based on various artificial intelligence (AI) techniques. Seven AI models were trained to predict the level of drought namely, adaptive neuro-fuzzy inference system (ANFIS), generalized linear model (GLM), deep learning model (DLM), decision tree model (DTM), random forest model (RFM), support vector machine (SVM), and artificial neural network (ANN). Moreover, different structures of fuzzy logic (FL) models were developed based on the causal relations between climatic conditions and drought. A comparative assessment was carried out between AI-based and conventional indices based on their correlation with multiple drought indicators. Nine conventional drought indices were selected for comparison namely, standardized precipitation index (SPI), percent of normal index (PNI), China-Z index (CZI), modified China-Z index (MCZI) rainfall anomaly index (RAI), Z-score index (ZSI), Palmer Drought severity index (PDSI), standardized precipitation evapotranspiration index (SPEI), and reconnaissance drought index (RDI). Historical rainfall and temperature data were collected for different Australian stations from 1995 to 2020, and utilized to train the AI models. Additionally, historical records for various drought indicators were used to evaluate the predictions of all models those included deep soil moisture (DI1), lower soil moisture (DI2), root zone soil moisture (DI3), upper soil moisture (DI4), and runoff (DI5). For the AI models, two outputs were utilized: the average normalized value of the three best-performing conventional indices, and the average output of the three best-performi