Optimizing Algorithms are crucial for solving complex numerical problems in many scientific and technical domains. In local and global search respectively, metaheuristic techniques Artificial Hummingbird Algorithm and the Particle Swarm Optimization (PSO) have demonstrated significant potential. Every technique has drawbacks, PSO might not be able to explore high-dimensional search spaces, whereas AHA frequently suffers from premature convergence, unconstrained optimization, and parameter Sensitivity. To improve optimization performance, a hybridized strategy study suggests that combination of AHA's exploratory nature with PSO's exploitative capabilities. Which improves convergence and resilience for high-dimensional and multimodal optimization issues. For real world optimization problem, such as cloud computing, autonomous systems, wireless sensor network, the suggested hybrid technique presents a sustainable answer.