This paper provides a detailed examination of a variety of deep learning techniques used to classify age and gender based on facial photos. The extensive use of deep convolutional neural networks (CNNs) has led to substantial advancements in computer vision, namely in areas like facial recognition. The objective of our study is to evaluate and contrast different convolutional neural network (CNN) architectures and methods for predicting age and gender. Our aim is to determine the most efficient strategy. We performed tests on standardized datasets, utilizing various network structures, training techniques, and data augmentation approaches. The results demonstrate disparities in performance among various models, underscoring the significance of architectural design and training approaches in attaining precise age and gender classification. This comparative research offers valuable insights into the benefits and limits of current deep learning methods for predicting age and gender from facial photos. It aims to assist in making well-informed decisions when developing future systems for facial analysis and recognition.