The Flood Algorithm (FLA) is a novel meta-heuristic optimization algorithm inspired by the movement and flow patterns of water during flooding events in river basins. It models key phenomena such as water movement toward slopes, flow rates over time, soil permeability, and periodic changes in water levels due to precipitation and losses. The algorithm guides a population of potential solutions toward optimal solutions by establishing a correlation between natural flood events and the optimization process. FLA operates in two phases: a regular movement phase where the population moves toward current best solutions, and a flooding phase that introduces random disturbances to increase diversity. New solutions are periodically introduced while weaker ones are removed, mimicking natural water level cycles. The algorithm's effectiveness is demonstrated through its application on benchmark optimization problems and engineering design problems. Extensive comparisons with 16 algorithms on CEC2005 functions and 20 algorithms on CEC2014 functions with dimensions 30, 50, and 100 confirm its robustness and strength. Furthermore, FLA's performance on 12 constrained engineering problems shows its ability to tackle real-world challenges. The FLA's source code is publicly available at https://www.optim-app.com/projects/fla. The algorithm addresses shortcomings in existing optimization methods, such as imbalance between local and global search, high computational time, and complex calculations. The proposed approach is a straightforward optimization method that aims to find optimal solutions efficiently.The Flood Algorithm (FLA) is a novel meta-heuristic optimization algorithm inspired by the movement and flow patterns of water during flooding events in river basins. It models key phenomena such as water movement toward slopes, flow rates over time, soil permeability, and periodic changes in water levels due to precipitation and losses. The algorithm guides a population of potential solutions toward optimal solutions by establishing a correlation between natural flood events and the optimization process. FLA operates in two phases: a regular movement phase where the population moves toward current best solutions, and a flooding phase that introduces random disturbances to increase diversity. New solutions are periodically introduced while weaker ones are removed, mimicking natural water level cycles. The algorithm's effectiveness is demonstrated through its application on benchmark optimization problems and engineering design problems. Extensive comparisons with 16 algorithms on CEC2005 functions and 20 algorithms on CEC2014 functions with dimensions 30, 50, and 100 confirm its robustness and strength. Furthermore, FLA's performance on 12 constrained engineering problems shows its ability to tackle real-world challenges. The FLA's source code is publicly available at https://www.optim-app.com/projects/fla. The algorithm addresses shortcomings in existing optimization methods, such as imbalance between local and global search, high computational time, and complex calculations. The proposed approach is a straightforward optimization method that aims to find optimal solutions efficiently.