Arctic sea ice thickness has been declining over recent decades due to climate change, making accurate prediction increasingly critical for environmental monitoring and climate modeling. Microwave remote sensing combined with machine learning has emerged as a promising approach for estimating sea ice thickness. This study investigates the prediction of lab-grown sea ice thickness (27–47 cm), using time-series backscatter data collected from surface-based Ku- and C-band scatterometers in three polarizations (VV, HH, and HV). A Random Forest model was applied to the time series, incorporating Normalized Radar Cross-Section (NRCS) values and statistical features (mean and standard deviation) across various temporal variables (lead and lag times). The model achieved high prediction accuracy, with the lowest error recorded at RMSE = 0.03 cm. Feature importance analysis using the Permutation Importance method revealed that co-polarized C-band features (C-VV and C-HH) were the most determining parameters in predicting sea ice thickness. These findings underscore the potential of integrating microwave remote sensing with Random Forest models to enhance sea ice thickness prediction and provide valuable insights for future research and real-time monitoring in Arctic regions.