Anemia is a global health issue characterized by low hemoglobin levels, affecting oxygen transport in the blood. Traditional diagnostic methods, like CBC, are accurate but require invasive procedures and expensive equipment. This study explores machine learning (ML) for non-invasive anemia estimation using wearable sensors, imaging, and predictive analytics. The proposed framework combines physiological data, demographics, and clinical history to train ML models for anemia classification and hemoglobin prediction. Data sources like PPG, pulse oximetry, and digital images of conjunctiva are used for feature extraction. Various ML algorithms, including SVM, random forests, and deep neural networks, are evaluated for performance. Preliminary results show ML-based methods can match traditional diagnostics in accuracy, with advantages in cost, portability, and scalability. The study highlights ML's potential for early anemia detection, especially in underserved areas. Future work will optimize the model for clinical use and real-time monitoring.