Abstract

The rapid evolution of bioanalytical techniques has generated vast datasets from complex biological systems, posing significant challenges for data interpretation. Machine learning (ML) and artificial intelligence (AI) have emerged as pivotal tools in addressing these challenges, enhancing the accuracy, speed, and scalability of bioanalytical methods such as mass spectrometry, next-generation sequencing, and biosensor assays. This article explores how ML and AI are transforming data interpretation in bioanalysis by automating pattern recognition, improving predictive modeling, and enabling real-time analysis. Through case studies and recent advancements, it highlights their contributions to biomarker discovery, disease diagnostics, and personalized medicine, while also addressing limitations and future directions. The integration of AI and ML into bioanalytical workflows promises to unlock deeper insights into biological processes, revolutionizing modern science and healthcare