Scots pine (Pinus sylvestris L.) wood is distinguished by its outstanding mechanical properties compared to other medium-density woods, and it is emerging as a material of choice in the woodworking community for its potential in modern construction practices. However, the milling processes for this valuable resource have yet to be optimised. This study examined the effectiveness of end milling in Scots pine, focusing on three key operational parameters: depth of cut, spindle speed, and cutting speed. The objective of this study was to systematically determine how these parameters influence the crucial milling performance quality metrics of cutting force and surface roughness, both independently and in combination. To extract the cutting parameters with the most significant impact on cutting force and surface quality, unsupervised machine learning tools for classification and prediction were applied, specifically, principal component analysis and projections to latent structures. This multivariate approach revealed that cutting force correlates positively with both cutting speed and depth of cut. Meanwhile, surface quality is mainly affected by depth of cut in a nonlinear manner. This study applied methods to assess the impacts of variable adjustments on milling outcomes resulting in guidelines for the woodworking industry to improve efficiency and product quality.