This study examines the mechanical performance of various lattice structures, highlighting the roles of geometric configurations, material properties, and processing conditions. Through advance feature importance analysis and correlation heatmaps, key determinants of mechanical behaviour such as lattice volume, surface roughness, micro-Vickers hardness, and lattice dimensions are identified. The findings reveal that lattice volume and surface roughness are the most significant predictors of displacement, accounting for 24.29 % and 23.71 % of the predictive power, respectively. An inverse relationship between micro-Vickers hardness and displacement emphasizes the need for material hardness optimization in design processes. Scatterplot analyses show complex interactions between surface roughness and micro-Vickers hardness across different conditions. Gyroid and BCC lattices exhibit inverse linear trends, while honeycomb and diamond display distinct linear and horizontal patterns, respectively. These complexities necessitate advanced analytical techniques like machine learning to capture and predict the mechanical behaviours of lattice structures effectively. Machine learning’s ability to handle high-dimensional data and identify non-linear relationships is crucial for accurate predictive models. The study also assesses the impact of post-processing conditions, indicating that stress relief and heat treatment enhances structural integrity and optimizes the material microstructure. This research provides a framework for designing and optimizing lattice structures, guiding informed decisions to meet performance standards.