In industrial manufacturing, job scheduling on uniform parallel machines poses a significant challenge that directly impacts productivity and operational efficiency. Traditional methods often struggle with the inherent complexity dynamic and NP-hard nature of modern industrial scenarios in resource allocation. This research investigates the application of swarm and metallurgy-inspired metaheuristic techniques to enhance job scheduling efficiency. Metaheuristics, known for their adaptability and capability to find near-optimal solutions, are employed to address the limitations of conventional methods. The study aims to maximize resource utilization, minimize production time, reduce delays, and meet deadlines by optimizing job allocation on uniform parallel machines. The research employs swarm-based metaheuristic algorithms like Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) algorithm and metallurgy-based algorithm like Simulated Annealing (SA) to achieve these objectives. These algorithms are trained on various datasets under different conditions to thoroughly evaluate their performance in minimizing critical metrics such as makespan and idle time. The results indicate that the ABC algorithm provides the most effective solution in specific scenarios. This research provides essential insights and practical advice for both researchers and practitioners in the manufacturing sector, demonstrating the application of advanced optimization techniques to improve job scheduling on uniform parallel machines.