The control of an inverted pendulum on a cart is a fundamental problem in control theory, widely studied due to its high nonlinearity, instability, and relevance in developing larger industrial systems, such as robotic manipulators, aerospace control systems, and autonomous vehicles. This paper explores three distinct control strategies for stabilizing and regulating the motion of an inverted pendulum: a Proportional-Integral-Derivative (PID) controller, an Integral Sliding Mode Controller (ISMC), and a Reinforcement Learning (RL)-based controller. A comparative analysis shows that while the PID controller provides basic stabilization, it struggles with strong nonlinearities. ISMC enhances robustness and disturbance rejection, ensuring stability under varying conditions. Through iterative learning, the RL-based controller adapts to system dynamics and achieves superior performance in complex scenarios. These results highlight the strengths and limitations of each approach, offering insights into their practical applications.