Diabetic Polyneuropathy (DPN) is a prevalent complication of diabetes, leading to significant sensory and motor dysfunction. Glycated Hemoglobin (HbA1c), as a biomarker in diabetes is widely used to assess clinical outcomes. The objectives were to synthesize literature on HbA1c levels and DPN using an AI-driven approach to identify trends and gaps and to analyze the relationship between elevated HbA1c levels and the severity of neuropathic symptoms. Our review highlights findings from various studies that demonstrate the critical role of glycemic control in mitigating neuropathic progression. By integrating AI methodologies, we also address the potential for predictive modelling and personalized treatment approaches based on individual HbA1c profiles. Ultimately, this review underscores the necessity for ongoing research to further elucidate the relationship between HbA1c and DPN, advocating for the implementation of AI tools in clinical practice to enhance patient management and outcomes in diabetic populations. Achieving near-normal HbA1c levels is crucial for preventing or delaying the onset of neuropathy in individuals with diabetes. The integration of engineering solutions and AI technologies can enhance monitoring and management, ultimately improving outcomes for patients at risk for diabetic peripheral neuropathy.