Additive Manufacturing (AM) has emerged as a transformative technology across multiple industries, offering design flexibility and functional integration beyond traditional subtractive methods. However, its widespread adoption remains limited due to challenges such as material selection, process parameter optimization, post-processing requirements, design constraints, and ensuring geometric fidelity in final components. Conventional laboratory-based trial-and-error approaches to address these challenges are resource-intensive, particularly for high-value materials such as platinum, which are widely used in medical, automotive, aerospace, and jewelry applications. The integration of Artificial Intelligence (AI), and specifically Machine Learning (ML), presents a promising approach to overcome these barriers. By leveraging cognitive capabilities to optimize process parameters and predict geometric outcomes, ML can reduce reliance on costly experimental iterations, enhance design fidelity, and improve production efficiency. This study explores the potential of AI-driven methodologies to streamline AM with platinum, reduce costs, and ensure geometric consistency, thereby accelerating the adoption of AM as a mainstream manufacturing strategy.