Drought is a critical environmental challenge in arid regions like Saudi Arabia, exacerbating water scarcity and threatening ecosystem stability. This study assesses short- and long-term drought severity in Saudi Arabia using the Standardized Precipitation Evapotranspiration Index (SPEI) at multiple timescales (2, 3, 6, and 12 months). Advanced statistical methods, including Innovative Trend Analysis (ITA), Wavelet Transform, and Bivariate Copula Models, were applied to analyze drought patterns, periodicity, and return periods. Results indicate significant negative trends in minimum SPEI values, confirming worsening drought conditions, particularly for SPEI-6 and SPEI-12. Wavelet analysis identified dominant drought periodicities of 1-3 years, linked to large-scale climate oscillations such as ENSO. The return period analysis using Gumbel and Beta distributions revealed that severe drought events (SPEI < - 2) have recurrence intervals exceeding 50 years, highlighting their rarity but extreme impact. These findings emphasize the increasing vulnerability of vegetation and water resources to prolonged dry conditions, underscoring the urgent need for adaptive water management strategies. The integration of remote sensing and statistical modeling in this study provides a robust framework for drought monitoring, offering valuable insights for policymakers and resource managers in arid regions. Future research should explore high-resolution climate modeling and machine learning-based drought forecasting to enhance predictive capabilities.