Diabetic foot ulcers (DFUs) present a critical challenge in diabetes care, often leading to severe infections and amputations due to delayed diagnosis. This systematic review aims to address gaps in DFU detection by evaluating emerging technologies designed to enhance early identification and improve patient outcomes. Implementing the PRISMA guidelines, 59 studies were selected for comprehensive analysis, focussing on the efficacy and clinical applicability of biomarkers, artificial intelligence (AI) and machine learning (ML) tools, and handheld diagnostic devices. Results indicate that biomarkers, such as procalcitonin, hold promise for detecting infections at an early stage. Moreover, AI and ML-based techniques substantially improve diagnostic accuracy and enable remote monitoring, facilitating timely intervention. However, challenges in integrating these technologies into routine clinical workflows persist due to cost, scalability, and infrastructure issues. Continued research is essential to address these limitations, ensuring that advanced DFU detection methods can be implemented effectively in diverse healthcare settings.