Globally, one of the most significant disasters is agricultural drought. The study area considered in this research paper is Pudukkottai District in Tamil Nadu State, India. The Bhalme and Mooley Drought Index (BMDI) is mostly practiced in India for assessing agricultural drought conditions using ground data. Availability of ground data is a serious concern in India, as it involves a laborious, tedious, and time-consuming data collection process. Remote sensing-based data collection will solve this problem. This study calculated the BMDI along with ten existing remote sensing-based agricultural drought indices (RSADIs) for the period from 2000-01 to 2022-23. The calculations were performed using Google Earth Engine (GEE) with Landsat imagery, and the RSADI produced results closer to BMDI were identified. The highest Pearson Correlation Coefficient (PCC) of 0.50 was obtained for NDTI with BMDI. Since the PCC value of 0.50 is less, NDTI cannot be accepted. Therefore, a new ANN-based RSADI with a higher PCC value of 0.87 with BMDI has been developed in this study.