Hydroxyapatite (HA), a bioactive ceramic, is widely used in biomedical applications, but high-temperature deposition can lead to phase degradation, affecting long-term stability. With a full factorial design, this study investigates HA coatings on Ti-6Al-4V substrates using high-velocity oxygen fuel (HVOF) spraying, incorporating machine learning for wear rate prediction. FESEM, XRD, and Raman spectroscopy confirmed the retention of the HA phase, with minimal shifts in m 1 (961.38   cm⁻¹ to 961.20   cm⁻¹) and m 4 (1048.33   cm⁻¹ to 1048.15   cm⁻¹) peaks. Wear mechanisms involved adhesive, abrasive, and third-body interactions, where larger debris acted as a solid lubricant. GMM-GBRT (R² = 0.94572) performed better than GMM-SVR (R² = 0.93195) in reducing prediction errors. SHapley Additive Explanations analysis identified fuel rate as the most influential parameter, followed by oxygen and powder feed rates. These findings confirm that HVOF spraying is a viable method for producing stable, high-performance hydroxyapatite (HA) coatings suitable for biomedical applications.