Osteoporosis leads to a decline in bone microarchitecture (MA), reducing bone density and increasing the risk of fractures. This condition is a serious public health concern, therefore detection of osteoporosis in early stages is crucial for preventing fractures and minimizing health impacts. This article highlights recent progress in osteoporosis detection (OD), covering both conventional and modern techniques. Dual-energy X-ray absorptiometry (DEXA) remains a widely used method for bone screening, with conventional X-rays still playing a key role in diagnosis. However, both methods have limitations in evaluating bone MA, necessitating improvements in imaging technologies. Advanced imaging modalities, such as computed tomography (CT), magnetic resonance imaging (MRI), and high-resolution peripheral quantitative CT (HR-pQCT), provide superior spatial resolution and allow for three-dimensional assessments of trabecular bone MA. These techniques offer deeper insights into bone quality and strength. Additionally, biochemical markers of bone turnover, like bone-specific osteocalcin and alkaline phosphatase have emerged as non-invasive indicators of bone health, complementing imaging in the detection of metabolic bone disorders. Machine learning (ML), a subset of artificial intelligence (AI), has shown potential in osteoporosis detection by enabling systems to analyze large datasets from imaging and clinical sources. ML-based texture analysis of trabecular bone MA offers accurate analysis of osteoporosis prediction based on fracture risk (FR). Regardless of these improvements, challenges remain in standardizing imaging protocols, refining computational methods, and incorporating new technologies into routine clinical practice. Collaboration among healthcare providers, researchers, and industry is essential to overcome these challenges and improve diagnostic accuracy.