Nowadays, hair loss plays a major problem in everyone’s life. Various factors, such as lifestyle habits and health conditions, are considered important contributors to individual hair loss. Modern technologies that enable early detection and customized therapies, such as AI-driven diagnostics, scalp imaging, and individualized treatment plans via the fields of blockchain, deep learning, and so on. Machine learning techniques provide better insights by analyzing complex relationships between the factors contributing to hair loss. This study uses machine learning algorithms to predict hair loss and identify its primary causes. However, earlier studies focus on limited datasets. The findings aim to support early detection by predicting hair loss and identify its key aspects by real-time data. Compared to traditional approaches, this project leverages feature selection and deep learning methods like Generative Adversarial Networks (GANs) to augment the dataset, enhancing prediction frameworks by using supervised machine learning techniques. The observation focuses on making improvements in prediction accuracy and feature relevance which make our solution more robust and practical.