Advanced Machine Learning Approaches for Biotechnology Quality Compliance in Future Prospects

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies in healthcare, biotechnology, and vaccine development, offering significant potential to improve process efficiency, decision-making, and resource utilization. This study presents Bio-MARL, an advanced machine learning framework for biotechnology quality compliance and biological process optimization that integrates multi-objective management with time-series prediction techniques. The proposed framework employs Long Short-Term Memory (LSTM) and Transformer-based models for temporal forecasting, combined with predictive maintenance strategies and multi-objective optimization to effectively manage operational trade-offs.  And the Productivity rose by 29.9% across datasets (Yeast 26.9%, E. coli 34.2%, and CHO 28.5%). and 94.8% of the batch was successful. Depending on the type of procedure, resource usage dropped by 20–25%. The design combines multi-objective methods that manage practical trade-offs, LSTM and Transformer models for temporal prediction, and predictive upkeep that cuts unscheduled downtime by 43%. Our method is validated by three industrial data sets: yeast generating enzymes on a large scale, E. coli producing the use of re and CHO cell cultures expressing monoclonal proteins. Together, the three datasets show steady gains in robustness, quality, and output. These findings show that smart automation may significantly boost supply-chain resilience and bio manufacturing profitability.