Soil salinity monitoring requires accurate measurement of saturated paste extract electrical conductivity (ECe) which is considered the most trustworthy measure of salinity hazard in many laboratories globally. It's a time-consuming and technically demanding process. In contrast, the measurement of EC values from the 1:1, 1:2.5, and 1:5 soil-to-water ratios is simple, rapid and cheap. This study aims to develop a pedotransfer functions to estimate ECe from diluted soil-to-water extracts (EC1:1, EC1:2.5, EC1:5) and presents an innovative FTIR spectroscopy approach coupled with machine learning for rapid EC prediction. A total of 59 soil samples were collected from 22 profiles across three depths (0-20, 20-50 and 50-100 cm) in Morocco and analyzed for EC at different soil-to-water extracts and scanned using a Bruker-Tensor-II-HTS-XT spectrometer. Random Forest (RF) and Partial Least Squares Regression (PLSR) models were employed to predict ECe and diluted EC values. Results demonstrated a significant linear correlation between the ECe and diluted extract EC values, with an R-2>0.89 across all extracts. In addition, the conversion factors (CF) (ECe = CF x EC (soil-to-water ratios)) varied significantly among soil types, indicating the critical role of soil-type parameters for accurate ECe estimation. FTIR developed models demonstrated high predictive accuracy across all soil-to-water extracts (R-2 = 0.86-0.91, RMSE = 0.41-3.69 dS/m), with distinct spectral features at 1970-2550 cm(-1) and 2867-3086 cm(-1) identified as the most sensitive regions for EC prediction. Random Forest (RF) models accurately predicted ECe from EC1:5 (R-2 = 0.92, RMSE = 2.05 dS/m), with enhanced performance when including CEC and CaCO3 content (R-2=0.95, RMSE=1.51 dS/m). In conclusion, FTIR spectroscopy combined with machine learning offers an accurate, rapid and minimal sample preparation method to make it particularly valuable for large-scale precision agriculture. Our findings demonstrate that mid-infrared spectroscopy enabling a rapid ECe estimation without saturated paste analysis a significant advancement for salinity hazard monitoring.