Control charts are essential tools used to keep track of and improve the quality of processes in various industries. They help monitor specific characteristics of a process to ensure everything stays on track. Sometimes, these characteristics are influenced by other factors, which makes regression-based control charts a good choice. However, a common challenge arises when there are outliers – data points that don’t fit the usual pattern. Outliers can throw off calculations, making traditional control charts less reliable. This research focuses on improving regression control charts so they can handle outliers better. By using advanced methods, the study introduces new types of robust control charts that are less affected by outliers. These charts use innovative techniques, including exponential-type kernel and mean absolute deviation estimators, to provide accurate results. To test these new charts, the study ran simulations, assessing how well they detect changes in processes under various conditions, including different sample sizes and levels of outliers. The results showed these robust charts perform better than traditional ones, making them a valuable tool for real-world applications. To demonstrate their usefulness, the researchers applied these methods to monitor temperature data in Sydney, Australia, showcasing how they improve decision-making in practical scenarios.