Machine learning is increasingly applied in chemical engineering to improve modeling, optimization, and prediction. The article discusses the current state of machine learning in chemical engineering, highlighting its potential and challenges. Machine learning models can process large datasets and identify patterns, making them useful for tasks like real-time optimization and chemical process control. However, chemical engineers often lack the necessary computer science and data analysis skills to fully utilize these tools. The article also addresses the limitations of machine learning, such as the black-box nature of many models, which can make interpretation difficult. Additionally, the quality of data is crucial for accurate predictions, and poor data can lead to unreliable results. The article emphasizes the need for better data curation, model interpretability, and collaboration between chemical engineers and data scientists. It also notes the importance of using machine learning in conjunction with traditional models to improve accuracy and reliability. The article concludes that while machine learning has significant potential in chemical engineering, its successful application requires addressing key challenges such as data quality, model interpretability, and interdisciplinary collaboration.Machine learning is increasingly applied in chemical engineering to improve modeling, optimization, and prediction. The article discusses the current state of machine learning in chemical engineering, highlighting its potential and challenges. Machine learning models can process large datasets and identify patterns, making them useful for tasks like real-time optimization and chemical process control. However, chemical engineers often lack the necessary computer science and data analysis skills to fully utilize these tools. The article also addresses the limitations of machine learning, such as the black-box nature of many models, which can make interpretation difficult. Additionally, the quality of data is crucial for accurate predictions, and poor data can lead to unreliable results. The article emphasizes the need for better data curation, model interpretability, and collaboration between chemical engineers and data scientists. It also notes the importance of using machine learning in conjunction with traditional models to improve accuracy and reliability. The article concludes that while machine learning has significant potential in chemical engineering, its successful application requires addressing key challenges such as data quality, model interpretability, and interdisciplinary collaboration.