Spectral computed tomography (CT) has become a sophisticated imaging technique that allows for both quantitative material characterisation and anatomical visualization by capturing data across several X-ray energy levels. The idea of "virtual pathology"—a non-invasive method of evaluating tissue composition and disease characteristics that previously needed biopsy—was born out of spectral CT, which, in contrast to standard CT, takes advantage of energy-dependent attenuation differences to discriminate tissue types. Spectral CT produces quantitative biomarkers, such as effective atomic number, electron density, and iodine concentration, that correlate with underlying disease alterations through methods like material decomposition, iodine mapping, and virtual non-contrast imaging. This capacity is based on dual-energy and photon-counting CT systems, which provide better material separation and spatial resolution than conventional detectors. Spectral CT has proven useful in clinical settings for oncology, cardiovascular illness, neurology, musculoskeletal imaging, and hepatobiliary assessment. It improves lesion detection and characterisation while lowering the need for additional imaging. Artificial intelligence and radiomics work together to support predictive, tailored therapy and improve automated tissue classification. Spectral CT is positioned as a possible supplement or substitute for histopathology biopsy in precision diagnostics despite technical and financial obstacles like cost, data requirements, and training needs, as well as ongoing validation studies and standardization initiatives.