This study uses the Standardized Precipitation Index (SPI) and machine learning model to monitor and forecast drought in arid and semi-arid regions. In the designated area drought phenomena are important for drinking water and agriculture. In particular, the study uses the SPI to forecast future instances of drought in the northern regions of Pakistan. We focused on enhancing the precision and quality of regional drought characterization while establishing a continuous monitoring process. A drought indicator termed the Artificial Neural Network based Quality Boosted Drought Index (ANN-QBDI) is introduced with the methodology involves assigning distinct weights to an ANN-based X-bar chart presenting it alongside regional precipitation aggregate data. Using the dataset of northern regions of Pakistan, applied the applications of ANN-QBDI. Through a pairwise comparison using the Pearson correlation coefficient, the study contrasts ANN-QBDI with the Regional Normalize Precipitation Index (RNPI). The ANN-QBDI exhibits more distinct regional characteristics in its correlations with other meteorological stations compared to RNPI along with a significantly lower Coefficient of Variation. These findings affirm ANN-QBDI as a valuable tool for regional drought analysis. The ANN-QBDI methodology introduces a unique approach to mitigating the impact of extreme values and outliers when aggregating regional precipitation data.