Continuous stirred tank reactors (CSTRs) exhibit nonlinear dynamic behavior and are used in most chemical industries. It’s a challenging task to design the controller for the CSTR model for controlling the temperature inside the reactor vessel which changes temperature due to exothermic reaction during product conversion from A to B. It has feed temperature as the manipulated variable. The conventional PID control strategy is widely used in industry, but its application for nonlinear systems does not provide the desired performance at all operating conditions. To overcome this problem, soft computing techniques like genetic algorithm (GA), particle swarm optimization (PSO), and fuzzy logic-based PID controllers are applied for the CSTR process as they give optimal values of controller parameters (Kc, Ki, Kd) that result in better setpoint tracking. The output from the controller is used to adjust the coolant or jacket flow rate to change the temperature of the jacket accordingly to control the temperature inside the reactor vessel at a desired value. A comparative study is carried out among the conventional Skogestad internal model control (SIMC)-based PID controller, metaheuristic, and fuzzy-based PID controllers with respect to time domain parameters such as rise time, response time, and error criteria. The implemented simulation studies done in MATLAB Intel core i5 processor prove that the soft computing techniques provide superior results.