Early identification of osteopenia, an easy indicator of osteoporosis, is essential for fracture preventative measures and the restoration of long-term bone wellness. This research sets up and assesses a machine learning screening framework designed for detecting the beginning of osteopenia, utilizing enhanced clinical datasets that include demographic, medical, and bone health variables. The Custom Weighted Ensemble Model, through a comparative analysis with different classification algorithms, obtained the highest accuracy of 98.69%, surpassing standard models including Random Forest and Gradient Boosting, both at 98.48%. The ensemble approach demonstrates enhanced precision and F1-scores, underscoring its effectiveness in balancing both precision and sensitivity compared with prior research which concentrates primarily on osteoporosis. This investigation demonstrates the initial stage of osteopenia detection along with it suggests that optimized ensemble strategies significantly improve predictive performance. The findings indicate the potential of data-driven models to enhance preventive bone health care and decrease the risk of future fractured bones. Early recognition of osteopenia—widely noticed as the impassive initial level for osteoporosis—is essential for safeguarding the health of bones and avoiding severe fractures. This paper presents a detailed review of existing methodologies for diagnosing and predicting osteopenia, with a focus on the comprehensive overview of machine learning (ML). By combining insights from significant studies on osteoporosis prediction and choosing a comprehensive dataset incorporating with demographic, medical, and bone health parameters, we reveal essential predictive factors. Additionally, we offer a state-of-the-art framework for creating reliable machine learning models specifically suited for early detection. This study highlights how data-driven innovation can improve clinical care, slow the progression of osteopenia, and pave the way for a future free of fractures.