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Age And Gender Prediction Using Facial Images Has Gained Significant Attention In Recent Years Due To Its Wide Range Of Applications In Fields Such As Human–computer Interaction, Security, Personalized Marketing, And Social Media Analytics. Traditional Computer Vision Techniques Often Struggle To Achieve High Accuracy Because Of Variations In Pose, Illumination, Facial Expressions, And Occlusions. To Overcome These Challenges, This Study Employs Deep Learning Models That Automatically Learn Discriminative Features From Raw Images Without Requiring Handcrafted Descriptors. Convolutional Neural Networks (CNNs) And Advanced Architectures Such As Residual Networks (ResNet) And Vision Transformers (ViT) Are Utilized For Robust Feature Extraction And Classification. The Models Are Trained And Validated On Benchmark Facial Image Datasets, Enabling The System To Predict Both Age Groups And Gender With High Precision. Experimental Results Demonstrate That Deep Learning–based Approaches Outperform Conventional Methods In Terms Of Accuracy, Generalization, And Scalability. The Proposed Framework Can Be Integrated Into Real-world Applications, Offering Reliable And Efficient Solutions For Automated Demographic Analysis.