This research focuses on contemporary strategies and methodologies in operations management, emphasizing resource allocation optimization. Key factors like demand variability, production capacity, and technological advancements are examined. The study explores decision-making processes in dynamic operational environments, balancing conflicting objectives such as cost minimization and service level maximization. It delves into hybrid models, specifically those combining artificial neural network (ANN) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), evaluating adaptability, efficacy, and associated advantages and limitations. The research provides insights guiding the feasibility and applicability of these models for resource optimization. Successful applications of hybrid ANN and TOPSIS models across industries are investigated, offering real-world examples, strategic applications, best practices, and challenges. The paper contributes valuable insights for organizations implementing similar approaches in operations management strategies.