Long-term drought forecasting plays a crucial role in mitigating drought risks by providing early warnings. Researchers have long been interested in achieving accurate long-term drought forecasting, which is challenging since accuracy generally decreases by increasing the forecasting period. The primary aim of this research is to propose a new method for high-accuracy long lead time drought forecasting by combining various Feature Extraction (FE) and selection techniques. In this study, monthly time-series datasets encompassing precipitation, potential evapotranspiration, actual evapotranspiration, runoff, surface and root-zone soil moisture-were utilized to forecast SPEI-6 over various lead times including 1-, 3-, 6-, 9-, 12-, 18-, and 24-months using global gridded products with a 0.5O x 0.5O spatial resolution spanning the years January 1980 to December 2022. The method was evaluated using two different approaches, namely Gaussian Process Regression (GPR) as a simple machine learning technique and Long Short-Term Memory (LSTM) as a deep learning method. The findings provided improved accuracy, particularly for long-term forecasting when employing the proposed methodology. When utilizing LSTM with FE instead of the original datasets as inputs, the error reduced from RMSE = 0.16 to RMSE = 0.07 (a 56 % decrease), while the correlation increased from R = 0.65 to R = 0.90 (a 38 % increase) when forecasting SPEI-6 12 months ahead. The results showed that the GPR with FE and selection model outperformed the LSTM with original datasets model for SPEI-6 (t + 24) with a correlation coefficient (R) of 0.9811 and a Normalized Root Mean Square Error (NRMSE) of 0.1380, compared to R = 0.6517 and NRMSE = 0.4307 for the LSTM with original datasets. These findings can offer valuable insights for early agricultural drought warning in arid areas.