This paper explores the application of machine learning (ML) and computer vision (CV) for detecting age and gender from facial images, aiming to enhance security measures and personalized user experiences. By evaluating a range of sophisticated algorithms, from classical ML to convolutional neural networks, this research seeks to bridge human-like perception with machine interpretation. A diverse, labeled dataset was compiled, with rigorous preprocessing to ensure image consistency. Models were trained and evaluated using performance metrics such as accuracy, precision, recall, and F1 score.



The findings demonstrate that ML and CV can achieve high accuracy in age and gender detection, sometimes surpassing human performance. These technologies have significant applications in digital marketing, healthcare, and public safety, where understanding demographics enhances service delivery and safety protocols. However, the research highlights challenges like data bias and ethical implications. Future work will focus on inclusive data collection and refining algorithms for fair, transparent, and unbiased outputs. This thesis aims to advance automated facial analysis and reevaluate ethical frameworks for deploying AI in sensitive applications.