AI-Assisted Uv–visible Spectroscopy for Rapid Quantification of Pharmaceutical Drugs

UV–Visible (UV–Vis) spectrophotometry is a rapid, economical and widely available technique for pharmaceutical analysis, but its selectivity is limited when the drugs in a formulation have overlapping absorption spectra. The integration of chemometrics and artificial intelligence (AI), including machine learning (ML) algorithms such as partial least squares (PLS), principal component regression (PCR), support vector regression (SVR), artificial neural networks (ANN) and random forests, with intelligent wavelength selection and metaheuristic optimization (genetic and Firefly algorithms), allows the simultaneous quantification of several active pharmaceutical ingredients (APIs) from the whole spectrum rather than a single wavelength. This review summarizes the principles of UV–Vis spectroscopy, the current status of AI-assisted UV–Vis analysis, and the challenges related to model interpretability, data integrity, validation and instrument variability. Applications, drawbacks and futureprospects are discussed, including process analytical technology (PAT), portable instruments, green analytical chemistry and regulatory acceptance of AI-enabled spectroscopic methods as complementary tools to chromatographic assays in pharmaceutical quality control.