Hyperspectral imaging (HSI) is a cutting-edge technology that captures detailed spectral information from objects or scenes. However, challenges such as high costs, large data files, complex data analysis, illumination variations, and spectral variability hinder its accessibility and effectiveness. This study addresses these challenges by employing dimension reduction techniques, namely Independent Component Analysis (ICA), Principal Component Analysis (PCA), and Factor Analysis (FA), to preprocess HSI data for document forgery detection. Machine learning algorithms including, Random Forest, Decision Tree, and Naive Bayes, are then utilized to develop the models. By applying these methodologies to the UWA-WIHSI dataset, superior performance of ICA-based models over existing state- of-the-art methods is demonstrated. The study emphasizes the importance of dimension reduction techniques and their integration with machine learning algorithms in advancing hyperspectral data analysis, enabling accurate interpretation of HSI data, and promising advancements in document forgery detection and related applications.