Osteoporosis is recognised as a civilization disease characterised by reduced bone mineral density (BMD) and an increased risk of fractures, particularly among postmenopausal women and the elderly. Current data indicate that a significant portion of individuals suffering from osteoporosis remains undiagnosed, highlighting the need for the development of diagnostic methods. This literature review comprehensively analyses existing diagnostic methods for osteoporosis. A literature review was conducted utilising electronic databases, such as PubMed and Google Scholar, to identify relevant articles published up to 2025. Articles were searched by combining related terms, such as “osteoporosis diagnostics”, “imaging diagnostics in osteoporosis” and “osteoporosis”. Dual-energy X-ray absorptiometry effectively assesses BMD and helps evaluate fracture risk; however, due to its limitations, diagnosis may not always be timely, necessitating the use of supplementary methods. Imaging technologies such as computed tomography, quantitative computed tomography, and magnetic resonance imaging are being investigated for their potential to provide additional insights into bone microarchitecture and health, while reducing patient exposure to ionizing radiation. Moreover, advancements in artificial intelligence (AI), including deep learning (DL), show potential for improving diagnostics. Studies suggest that DL models demonstrate high sensitivity and specificity, creating potential for the development of screening tools using imaging data obtained during routine examinations. This article provides a critical review of current diagnostic possibilities in osteoporosis, emphasising the importance of a multifaceted approach that integrates traditional imaging and modern AI-based methodologies to enhance the early detection, prevention, and treatment of osteoporosis.