Machine learning models have been leveraged to accelerate biotech research and diagnostics. Applications include early prediction of stem cell-derived organoid quality, restaurant demand forecasting based on weather data, AI-enabled deep learning frameworks for tumor profiling, and the integration of AI-powered process modeling in bioprocess engineering via the Repligen-Novasign partnership. These examples demonstrate AI's growing role in enhancing predictive accuracy, operational efficiency, and research throughput in biomedical and biotech fields.