Journal of Pharmaceutical Analysis and Drug Research ISSN: 3108-0839 (Online)

Authors: Dr. Ananya Mukherjee, Dr. Rakesh Sharma

Abstract:  Chemometric and machine learning methods are increasingly applied across diverse scientific and engineering disciplines for data analysis, predictive modeling, and decision-making. While chemometrics primarily focuses on extracting meaningful information from chemical data through statistical and mathematical tools, machine learning enhances this process with computational intelligence and algorithmic adaptability. Together, these methods provide robust frameworks for handling high-dimensional, complex, and noisy datasets. This paper discusses the theoretical foundations of chemometric and machine learning methods, their applications in different fields, comparative strengths, and emerging trends. The challenges and limitations associated with model interpretation, overfitting, data preprocessing, and computational complexity are also highlighted. Finally, the paper explores future prospects in integrating chemometric strategies with machine learning paradigms for advancing research in pharmaceuticals, material science, food chemistry, environmental studies, and bioinformatics.

Keywords: Chemometrics, Machine Learning, Data Analysis, Predictive Modeling, Pattern Recognition, Artificial Intelligence, Multivariate Analysis, Computational Chemistry