CSTR is a unit where highly exothermic reactions take place and final product is obtained by maintaining a specific temperature. This can be challenging, so various control approaches have been developed with improved versions and applied by changing the operating conditions. PID is one of them and commonly used in process industry for many years. Conventional controllers provide satisfactory results but achieving appropriate control considering variations in operating point and environmental conditions is not possible. Hence, researchers have put enormous efforts to develop controller with intelligent features of GA, PSO, ABC, TLBO fuzzy logic, neural networks to auto-tune parameters of PID and achieve desired results. These algorithms are known as nature-inspired or population-based optimization algorithm. They are employed with population sizes, uncertainties, probabilities, iterations which help find the desired solution for a problem [1].In optimization techniques, the initial population is a set of candidate solution which creates a new population based on the fitness. The resultant is an optimal solution with efficient performance. The cycle repeats until its true condition is reached. These methods can be categorized in artificial intelligence as Swarm Intelligence and Evolutionary Algorithms [1]. TLBO was first introduced by Professor R. Venkata Rao in 2011. It is a self-learning nature inspired algorithm which uses population size and iterations in a problem for process control. These parameters are also called as design variables for TLBO method. TLBO works on a practical example of impact of teacher on students in a class. The students are considered as population, subjects offered to them are the design variables and the students result is the fitness score