This chapter highlights the historical, current, and emerging trends on soft sensors. Soft sensors are computer programs that use mathematical models to estimate process variables or quality attributes in real time. They are widely used in the chemical, pharmaceutical, and food industries to improve process control and product quality. Recent advancements in soft sensors have led to improvements in accuracy, robustness, and speed of estimation. One of the key advancements in soft sensors is the use of machine learning techniques, such as artificial neural networks, support vector regression, and deep learning. The failures and past achievements are essential for the future planning; therefore, this chapter aims to provide a comprehensive overview of the advancements, challenges, and opportunities in soft sensor research, with the goal of encouraging further research and development in this important field.