The accurate extraction of photovoltaic (PV) cell parameters is essential for optimizing the efficiency and performance of solar energy systems. This study introduces the Tiki-Taka Algorithm (TTA), a novel metaheuristic inspired by the strategic principles of the Tiki-Taka football strategy, to enhance parameter extraction processes. TTA employs techniques of short passing and dynamic positioning to navigate complex optimization landscapes efficiently, addressing the nonlinearities and variabilities inherent in PV cell models to provide precise parameter estimations. We evaluate the performance of TTA against four well-known metaheuristic algorithms: Particle Swarm Optimization (PSO), Atomic Orbital Search (AOS), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA). The comparison is based on key metrics, including the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R2), and execution time. Our findings reveal that TTA demonstrates competitive precision and computational efficiency compared to other algorithms. MATLAB Simulink is utilized for the simulations, providing a robust environment for testing and validation. By leveraging the TTA, we aim to contribute a robust and efficient tool for parameter extraction in the solar energy sector, thereby enhancing the optimization and performance of PV systems.