The Ground Reaction Force (GRF) signals contain valuable biomechanical information that can be used for human classification tasks. This study investigates the use of Random Forest, Neural Network, and 1D Convolutional Neural Network (1D CNN) models to predict gender based on GRF data. The models are trained and evaluated on a large dataset consisting of six standardized GRF features from both feet. Experimental results demonstrate that deep learning models, particularly the 1D CNN, are highly effective in capturing complex GRF patterns and achieve superior classification performance compared to traditional machine learning approaches. These findings confirm the potential of GRF-based approaches for accurate and reliable gender classification.