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IoNT, has found very similar real-life applications in agriculture, military, multimedia, and healthcare. However, despite the rapid advancements in both IoNT and machine learning (ML), there has been no comprehensive review explicitly focused on the integration of these two fields. Existing surveys and reviews on IoNT primarily address its architecture, communication methods, and domain-specific applications, yet overlook the critical role ML could play in enhancing IoNT\u2019s capabilities\u2013particularly in data processing, anomaly detection, and security. This survey addresses this gap by providing an in-depth analysis of IoNT-ML integration, reviewing state-of-the-art ML applications within IoNT, and systematically discussing the challenges that persist in this integration. 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