The chemical industry is the backbone of global manufacturing, driving innovation across multiple sectors. Since chemical processes are complex and dynamic in nature, it is still difficult to maintain efficiency, consistency in product, and optimize process parameters. Traditional approaches often fall short in handling these complexities, prompting manufacturers to adopt data-driven methodologies, including statistical models, machine learning techniques, and deep learning architectures. This survey discusses how these models help in fault detection, process optimization, and quality control. We examine the role of statistical models in capturing process variation, machine learning models in detecting patterns and anomalies, and neural networks in predictive maintenance and real-time monitoring. Additionally, we explore fusion-based architectures, including hybrid statistical, machine learning, and deep learning methods, that facilitate better fault detection and parameter estimation. The survey also highlights how data-driven approaches support sustainable chemical manufacturing by enabling real-time decisions, adaptive control, and effective process monitoring.
