Delayed skin wound healing remains a significant clinical challenge due to the complex interaction between local wound microenvironment instability, systemic inflammatory responses, and patient-specific comorbidities. This prospective comparative study evaluated whether integration of sensor-based wound monitoring with an artificial intelligence-driven clinical decision support system (AI-CDSS) improves regenerative outcomes compared with conventional care. A total of 120 patients were allocated into three groups (n=40 each): standard wound care, standard care with sensor-based monitoring, and sensor monitoring integrated with AI-CDSS. Dynamic clinical parameters, microenvironment indicators (temperature, pH, moisture), and immunological markers (CRP, IL-6, TNF-α, IL-10) were assessed over 28 days. By Day 14, mean wound area reduction reached 67% ± 17 in the sensor + AI group compared with 52% ± 18 in the sensor-only group and 38% ± 16 under standard care. Complete wound closure by Day 28 occurred in 75.0% of patients in the AI-supported group versus 60.0% and 45.0% in the sensor-only and conventional groups, respectively. Systemic therapy escalation was reduced to 5.0% in the AI group compared with 20.0% in standard care. The AI model demonstrated strong predictive performance for delayed healing (accuracy 86.7%, sensitivity 84.0%, specificity 88.5%, AUC 0.91) and provided a median alert lead-time of 3.2 days before clinical deterioration. These findings indicate that combining real-time sensor data with AI-based interpretation accelerates microenvironment normalization, reduces inflammatory burden, improves epithelialization dynamics, and enhances overall healing efficiency. The integrated sensor-AI approach represents a data-driven strategy for precision wound management and resource optimization in modern regenerative medicine.