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The pursuit of sustainability in analytical sciences has accelerated the adoption of green analytical chemistry (GAC), which emphasizes minimizing environmental impact without compromising analytical performance. Within this framework, chemometrics has emerged as a pivotal enabler, offering systematic, data-driven approaches to optimize methods, reduce resource consumption, and enhance eco-efficiency. By employing exploratory tools such as principal component analysis and hierarchical cluster analysis, chemometrics facilitates pattern recognition and dimensionality reduction, while regression and calibration models, including partial least squares, principal component regression, and artificial neural networks, enable accurate prediction and quantification with minimal experimentation. Optimization strategies such as design of experiments, response surface methodology, and evolutionary algorithms further reduce solvent and reagent usage, leading to greener method development. This chapter critically explores how chemometric tools support waste minimization, resource management, and multicriteria sustainability assessment through frameworks like Analytical Eco-Scale, GAPI, and AGREE. Applications across chromatography, spectroscopy, electrochemistry, and sensor-based platforms highlight their broad relevance. Challenges such as data quality, overfitting, and industrial adoption barriers are discussed alongside emerging prospects in artificial intelligence, machine learning, and digital twins. Collectively, chemometrics redefines analytical innovation by uniting precision, efficiency, and sustainability, positioning it as a cornerstone of future smart and green laboratories.