Dryland ecosystems are highly vulnerable to increased aridity, thus exacerbating the drought stress. From this perspective, our study aimed to evaluate the aridity index (AI) and Standardized Precipitation Index at a three-month scale (SPI-3) across three arid stations of Pakistan from 1990 to 2023. Seven remote sensing indices were used as covariates with SPI-3 and mean temperature for enhanced prediction. Four well-optimized machine learning models were employed on seasonally decomposed time series. Mann-Kendall and Sen's slope analysis revealed a significant (p < 0.001) increasing trend of AI and SPI-3 values, indicating a comparatively lower aridity in recent years. It was consistent with the increasing trend of NDVI with Sen's slope range from 0.0002 to 0.003. Cross correlation showed a seasonal effect of biophysical indicators on AI with a positive correlation of r = 0.4 with NDVI and r = 0.6 with NDWI at lag 0, indicating a late lag effect. Furthermore, machine learning prediction of AI with a three-month lag size revealed an outperformance of Gradient Boosting Regression with R-2 = 0.806 and RMSE = 0.076, followed by Random Forest with R-2 = 0.732 and RMSE = 0.089. The Dry Barren Soil Index (DBSI), NDWI, and SPI-3 gained high feature importance in the highly performed model. Our study highlights the significance of temporal and seasonal relationships of aridity and biophysical indicators in dryland ecosystems, informing region-specific land and water resource management policies to mitigate hydroclimatic extremes.