<![CDATA[Titanium alloys—especially Ti-6Al-4V—play a pivotal role in aerospace, biomedical, and energy applications due to their high strength-to-weight ratio and corrosion resistance. However, recurring key limitations remain: high wear/friction, “difficult-to-machine” behavior (localized heat, adhesion, rapid tool wear), and the sensitivity of surface integrity to process parameters. This review presents a comprehensive synthesis based on the provided literature (BUDI as the main review/extraction source and DATA 1 as additional recent abstracts), with emphasis on findings from 2024–2026. We map four major clusters: (1) materials and microstructure engineering (e.g., high-pressure sintering, multicomponent solid solutions), (2) surface engineering and coatings to enhance hardness/wear resistance (e.g., diffusion layers, micro-arc coatings), (3) machining and forming processes (turning/milling/drilling/thread rolling) including wear mechanisms, surface quality, and productivity–tool-life trade-offs, and (4) the use of machine learning (ML) and explainable/decision-support approaches for damage prediction, wear classification, and parameter optimization. The main contribution of this review is cross-cluster integration: linking physical mechanisms (tribology, thermal effects, deformation) with data-driven frameworks (explainable ML) as a bridge toward “manufacturing intelligence.” We also highlight research gaps, including limited data standardization (sensors, wear labels), insufficient model generalization across machines/materials, and a lack of sustainability assessments that are directly connected to operational decision-making. This review provides extensive summary tables to guide researchers and industry in designing more precise, reproducible, and readily translatable strategies to improve Ti-6Al-4V performance.]]>
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