Flower Pollination Algorithm (FPA) is a nature inspired metaheuristic algorithm proposed by Xin-She Yang in 2012. It uses two types of local and global pollination mechanisms and borrows them from the pollination of flowering plants. FPA suffers from two problems i.e., slow convergence rate in high-dimensional spaces and it has a high probability to become stuck in local minima. We can improve this by hybridizing FPA with Genetic Algorithm, Particle Swarm Optimization and changing population distributions using various functions. We have thoroughly assessed the deviations on 8 scalable dimensional multi-modal functions. It demonstrates robustness with respect to base FPA, Hill Climbing, and Tabu Search. Along with functions, to validate the real-world efficacy we have tested Hybrid Algorithms on high-dimensional datasets. Wine Quality, Sensor less Drive, Bank Marketing achieving an improvement of 85%, 10%, and 9% respectively in the MSE. This shows the algorithm’s ability to handle high-dimensional data making it suitable for Industrial Fault Detection, Process Optimization.