Facial recognition technology has undergone significant advancements, particularly through the utilization of machine learning and deep learning techniques. This paper introduces a robust Convolutional Neural Network (CNN) model for real-time age, gender, and emotion recognition in facial images, aiming to overcome limitations in traditional methods. By leveraging hierarchical image representations, the CNN model autonomously learns intricate facial features, enabling precise identification across diverse demographic groups. Extensive data augmentation techniques expanded the dataset to 234,400 images, amalgamated from the UTK Faces and Facial Age datasets for age detection and the CK+ dataset for emotion recognition. The proposed methodology involves training separate CNN models for age, gender, and emotion classification, each with specific input sizes and architectures, followed by integration into a unified system for comprehensive facial analysis. The age prediction model achieved an accuracy of 0.77, the gender classification model demonstrated an impressive 0.89 success rate, and the emotion recognition model exhibited an outstanding accuracy of 0.97. Integration of these models into a unified system showcased remarkable performance, providing a holistic solution for multi-task facial analysis. This research underscores the potential of deep learning techniques in facial analysis, with promising implications for various real-world applications.