This scoping review explores the current methodologies and strategies for optimizing the injection molding process, with a particular focus on tensile strength and critical process parameters such as “mold temperature, packing pressure, and cooling time”. A comprehensive analysis of 13 studies, spanning materials like polypropylene (PP) and low-density polyethylene (LDPE), reveals that the Taguchi method is the most commonly employed optimization technique, often combined with advanced approaches such as “Artificial Neural Networks (ANN) and Principal Component Analysis (PCA)”. The review highlights the importance of optimizing these parameters to improve product quality, reduce manufacturing costs, and minimize material wastage. It also identifies emerging trends, including the increasing interest in the integration of machine learning and sustainability in material selection. Future research prospects include hybrid optimization techniques and real-time process monitoring, both of which have the potential to further enhance the efficiency and precision of injection molding processes. These insights provide a foundation for future advancements, offering a roadmap for the continued evolution of injection molding optimization in various industries.