This review discusses the application of machine learning (ML) techniques to improve nanosensors in agroenvironmental applications. Nanosensors, which use nanomaterials to detect analytes, face challenges such as weak signals and noise, making data interpretation difficult. ML algorithms, including supervised and unsupervised learning, have been increasingly used to enhance the accuracy and functionality of nanosensors. The review covers various nanosensor types, such as electrochemical, luminescent, SERS, and colourimetric, and discusses how ML techniques are applied to these systems. Examples include the use of artificial neural networks (ANNs), support vector machines (SVMs), and random forests for data analysis and classification. The review also highlights the benefits of ML in improving the sensitivity and specificity of nanosensors for detecting agricultural and environmental parameters. The integration of ML with nanosensors enables more efficient and accurate monitoring of agroenvironmental conditions, contributing to sustainable agriculture and environmental protection. The paper concludes that ML significantly enhances the performance of nanosensors, offering a promising approach for future agroenvironmental applications.This review discusses the application of machine learning (ML) techniques to improve nanosensors in agroenvironmental applications. Nanosensors, which use nanomaterials to detect analytes, face challenges such as weak signals and noise, making data interpretation difficult. ML algorithms, including supervised and unsupervised learning, have been increasingly used to enhance the accuracy and functionality of nanosensors. The review covers various nanosensor types, such as electrochemical, luminescent, SERS, and colourimetric, and discusses how ML techniques are applied to these systems. Examples include the use of artificial neural networks (ANNs), support vector machines (SVMs), and random forests for data analysis and classification. The review also highlights the benefits of ML in improving the sensitivity and specificity of nanosensors for detecting agricultural and environmental parameters. The integration of ML with nanosensors enables more efficient and accurate monitoring of agroenvironmental conditions, contributing to sustainable agriculture and environmental protection. The paper concludes that ML significantly enhances the performance of nanosensors, offering a promising approach for future agroenvironmental applications.