<p>Turning processes face variability in machining conditions and tool wear, reducing productivity and accuracy. Monitoring and optimization methods using sensors, models, and design techniques have improved performance, yet challenges remain from sensor noise, model reliability, and limited adaptability under changing conditions. This study presents an integrated cutting force prediction model that incorporates tool-wear effects to enhance machining efficiency across various conditions. By utilizing an orthogonal cutting force model, this research predicts the cutting forces generated under specific cutting conditions. It specifically addresses adhesive-, abrasive-, and diffusion-wear mechanisms, all of which significantly influence flank wear. Modeling these mechanisms allows for the accurate prediction of cutting force variations as tool wear progresses, enabling effective tool condition monitoring. Based on this wear-informed model, a feed-rate-optimization strategy was implemented to maintain cutting forces within a target range, particularly in roughing operations. By utilizing root-finding functions were used to optimally adjust feed rates along different machining paths, resulting in a cycle-time reduction of approximately 15.21%. Experimental validation, including scanning optical microscopy observations, confirmed the model’s capability to accurately estimate tool wear and optimize machining conditions without requiring additional sensors.</p>