This work constructs a multi-output Convolutional Neural Network (CNN) for demographic classification, including age estimation, gender determination, race categorization, and geographical subcategories of South Asia, North Asia, Europe, and Africa. Sub-region classification is further carried out in the Indian subcontinent: Karnataka, Kerala, Tamil Nadu, Punjabi and Kashmiri. The proposed CNN is trained using a heterogeneous dataset and its performance is compared with some robust pre-trained models such as VGG16, ResNet50, InceptionV3, and DenseNet121. The proposed custom CNN architecture makes use of convolutional layers for feature extraction, dropout for regularization, and custom loss weights for balanced multi-task learning. Experimental results are showing high accuracy values of 97% for age estimation, 99.55% for gender classification, and 98.4% for region and sub-region classification. The performance of the model gives a sign of its ability to handle demographic diversity while striving to minimize bias compared to its currently existing solutions. This work provides a foundation for future advancements in hierarchical facial recognition and demographic classification with an emphasis on ethical AI and fairness in real-world applications, including overcoming dataset limitations, exploring hybrid models, and incorporating multimodal data to give more inclusivity and precision.