Diabetic Polyneuropathy, a prevalent complication of Diabetes Mellitus, leads to significant motor and sensory deficits, particularly in the feet. These deficits can result in gait disturbances, imbalance, increased fall risk, and reduced quality of life. This study aims to assess the effect of sensorimotor training on plantar pressure sensitivity and functional mobility in individuals with Diabetic Polyneuropathy, with the support of artificial intelligence (AI) for data analysis. Seventy participants (male and female) were recruited and randomly assigned to two groups: Group A (n = 35) underwent sensorimotor training, while Group B (n = 35) received conventional training. Both interventions lasted 45 min, three times a week for 12 weeks. AI algorithms were used to analyze pre- and post-intervention data, focusing on plantar pressure sensitivity assessed by the 10 g Semmes Weinstein Monofilament and functional mobility evaluated using the Timed Up and Go (TUG) test. Post-intervention analysis revealed significant improvements in both groups for Semmes Weinstein Monofilament testing (Group A: p = 0.000; Group B: p = 0.014) and TUG values (Group A: p = 0.000; Group B: p = 0.000). However, comparisons indicated that Group A had significantly greater improvement in both outcome measures (p = 0.000), as identified through AI-driven statistical analysis. The results suggest that sensorimotor training is significantly more effective than conventional training in enhancing plantar sensitivity and functional mobility in individuals with Diabetic Polyneuropathy. AI’s role in data analysis facilitated better understanding of these improvements, paving the way for future personalized treatment approaches.