Age, gender, and race classification pose challenges across various domains, including computer vision, social sciences, and marketing. Despite advancements in deep and machine learning, convolutional neural networks (CNNs) remain crucial for addressing these tasks. This paper introduces an innovative approach utilizing CNNs with residual blocks to enhance accuracy and efficiency in age, gender, and race classification. Incorporating residual connections enables the model to capture both low-level and high-level features, improving classification accuracy while minimizing computational complexity. The residual blocks facilitate the learning of residual mappings, aiding gradient propagation and enabling successful training of deeper networks. Evaluation of the model on dataset FairFace demonstrates the model’s performance with accuracies of 56.3%, 94.6%, and 58.4% for age, gender, and race, respectively.