Recent years have seen the rise of sophisticated materials in design and manufacture. They are crucial in many industries because of their strength, fexibility, and durability. While efective, conventional techniques of describing these qualities may be time-consuming and may not fully use the considerable quantity of data available. Integrating machine learning and intense learning with material science processes may solve the issue. In this research, we use convolutional neural networks (CNNs), known for image and pattern recognition, to understand material microstructure patterns and forecast their mechanical properties. PSO is used to enhance and optimize these predictions. Our technique uses a CNN-based system to create, train, and validate models for material property predictions and a unique PSO-CNN integration to maximize model parameters and prediction accuracy. Our studies showed that the dataset's mean ultimate strength is 572.75 MPa, a standard for CNN training. Data variability necessitates complex CNN architectures with PSO-optimizing parameters. Poisson's ratio and density fuctuations suggest material modifcation. This study establishes a framework for characterizing and optimizing mechanical characteristics in design materials and links computational methods to real-world material science applications. Keywords Mechanical properties · Design materials · Convolutional neural networks · Particle swarm optimization · Optimization