A narrative review of machine learning integrated electrochemical sensors for smart environmental monitoring

Review paper

Authors

  • Shashanka Rajendrachari Department of Chemistry, Global Academy of Technology, Bangalore-560098, Karnataka, India https://orcid.org/0000-0002-6705-763X
  • Rajamouli Boddula Department of Chemistry, School of Sciences and Humanities, SR University, Warangal, Telangana 506371, India https://orcid.org/0000-0003-0414-715X
  • Hareesha Nagarajappa Department of Chemistry, FMKMC College, Mangalore University, Karnataka, India https://orcid.org/0000-0002-6289-6119
  • Ersin Demir Department of Analytical Chemistry, Faculty of Pharmacy, Afyonkarahisar Health Sciences University, Afyonkarahisar 03100, Türkiye https://orcid.org/0000-0001-9180-0609
  • Antherjanam Santhy Department of Chemistry, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam 690525, India https://orcid.org/0000-0002-7748-7975
  • S. R. Kiran Kumar Department of Chemistry, Center for Nano Science, K.S. Institute of Technology, Bangalore-560109, Karnataka, India https://orcid.org/0000-0002-6176-3189

DOI:

https://doi.org/10.5599/jese.3492

Keywords:

Nanomaterial-based sensors, smart sensing systems, electrochemical data analysis, artificial intelligence, Internet of Things

Abstract

In the recent era, industrialization, urbanization, and unethical agricultural practices have caused environmental degradation and therefore, there is an utmost need for fast and efficient monitoring systems. The solution to the above is electrochemical sensors; these are one of the useful analytical tools used for environmental monitoring because of their high efficiency, sensitivity, less tedious, cost effective. However, these electrochemical sensors can experience signal noise, interference peaks from complex mixtures of analytes, and interpretation problems, especially with nonlinear electrochemical responses. In recent years, machine learning (ML) has become one of the most groundbreaking methods to improve the performance of electrochemical sensors by making it easy for complex signal processing, pattern recognition, and highly accurate modelling. Combining ML with electrochemical sensors enables advanced environmental monitoring with real-time analysis, simultaneous determination of multiple analytes, and improved selectivity and sensitivity. This review provides a detailed explanation of ML-based electrochemical sensors and their advantages and applications in environmental monitoring. This article also discusses the advantages of ML integration with nanomaterials and the Internet of Things for determining toxic dyes, pesticides, heavy metals, etc.

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References

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Published

25-08-2026

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Vol. 16 (2026)

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Electroanalytical chemistry

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Copyright (c) 2026 Shashanka Rajendrachari, Rajamouli Boddula, Hareesha Nagarajappa, Ersin Demir, Antherjanam Santhy, S. R. Kiran Kumar

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How to Cite

A narrative review of machine learning integrated electrochemical sensors for smart environmental monitoring: Review paper. (2026). Journal of Electrochemical Science and Engineering, 16, Article 3492. https://doi.org/10.5599/jese.3492

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