This paper reviews the application of machine learning techniques to improve the performance of nanosensors in agroenvironmental applications. Nanosensors, which are devices that use nanomaterials to detect analytes, have gained increasing attention due to their potential in various fields, including agriculture. However, challenges such as noise and confounding signals have hindered their practical use. Machine learning, particularly supervised and unsupervised learning, has emerged as a powerful tool to enhance the quality and functionality of nanosensor systems. The review covers the latest advancements in using machine learning to analyze data from different types of nanosensors, including electrochemical, luminescent, surface-enhanced Raman spectroscopy (SERS), and colorimetric sensors. Examples of successful applications are provided, demonstrating how machine learning can improve signal detection, classification, and regression tasks. The paper concludes by discussing the relevance of these techniques to the future of the agroenvironmental sector, highlighting their potential to enhance food safety, environmental monitoring, and agricultural sustainability.This paper reviews the application of machine learning techniques to improve the performance of nanosensors in agroenvironmental applications. Nanosensors, which are devices that use nanomaterials to detect analytes, have gained increasing attention due to their potential in various fields, including agriculture. However, challenges such as noise and confounding signals have hindered their practical use. Machine learning, particularly supervised and unsupervised learning, has emerged as a powerful tool to enhance the quality and functionality of nanosensor systems. The review covers the latest advancements in using machine learning to analyze data from different types of nanosensors, including electrochemical, luminescent, surface-enhanced Raman spectroscopy (SERS), and colorimetric sensors. Examples of successful applications are provided, demonstrating how machine learning can improve signal detection, classification, and regression tasks. The paper concludes by discussing the relevance of these techniques to the future of the agroenvironmental sector, highlighting their potential to enhance food safety, environmental monitoring, and agricultural sustainability.