This study aims at investigating worn surface topography and mathematical modeling of annealed Ti-6Al-3Mo-2Sn-2Zr-2Nb-1.5Cr alloy using response surface methodology (RSM). The alloy was subjected to three different regimes in order to study their effect on mechanical properties. First regime was applying cold deformation by compression until 15% drop in height at room temperature. The second regime was performing solution treated on the deformed samples at 920 °C for 15 min then air-cooled (AC) to ambient temperature. Third regime was applying aging on the deformed and solution treated specimen for 4 hr at 590 °C followed by air-cooling. Three different velocities (1, 1.5, and 2 m/s) were adopted to conduct dry sliding wear according to the experimental design technique (EDT). Gwyddion and Matlab softwares were used to detect worn surface photographs analytically and graphically. Maximum hardness of 425 HV20was obtained for AC+Aging specimen, while minimum hardness of 353 HV20was reported for the annealed specimen. Applying aging process after solution treatment enhanced considerably the wear property and this enhancement reached 98% as compared to the annealed condition. The relationship between input factors (hardness & velocity) and responses (Abbott Firestone zones) was demonstrated using analysis of variance (ANOVA). The best models for Abbott Firestone zones (high peaks, exploitation, and voids) produced accurate data that could be estimated for saving time and cost. The results showed that the average surface roughness increases with increasing sliding velocity for all conditions except AC+Aging condition where the average surface roughness decreased with increasing sliding velocity. The results revealed that at low velocity and hardness, the material gives the highest exploitation zone (86%). While at high velocity and hardness, the material gives the lowest exploitation zone (70%). In general, the predicted results of mathematical model showed close agreement with experimental results, creating that models could be utilized to predict Abbott Firestone zones satisfactorily.