###### Abstract
However, these models may not capture the full complexity of the emission formation process, leading to potential discrepancies between estimated and actual emissions [23]. The emergence of machine learning can address the above challenges for PEM by providing more reliable process parameters and advanced model algorithms for data processing and interpretation, which significantly help improve the accuracy and effectiveness of PEM. Machine learning is an area of artificial intelligence (AI) and computer science concerned with the processing of data and algorithms that allow AI to mimic the way humans learn, with the goal of gradually increasing its accuracy [28,29]. The nature of ML's capability to process and interpret large datasets intelligently enables it to be a promising technology to transform emission monitoring systems by enhancing their precision, effectiveness, and cost-efficiency [30,31]. Figure 1 shows the general flow process of machine learning model development, where the feedback loops across the model development phases are key enablers for monitoring the capability and reliability of the developed machine learning model. \"Data preparation\" is the first step for developing a typical machine learning model, and the data can be historical data generated from power plants in long-term operation. \"Data processing\" cleans and filters out any deficient data by using different machine learning algorithms, which will be detailed in later sections. After parameter tuning, the machine learning model is trained and tested for validation. This process may need to be repeated until a desired level of model accuracy is achieved. The utilization of AI technologies should help to improve the efficiency of fossil fuel power plants for emission control by overcoming the limitations of traditional methods and providing power plant operators with more accurate, reliable, and actionable emission data. By leveraging AI-based approaches, power plants can optimize their emission control strategies, reduce costs associated with monitoring and compliance, and ultimately contribute to the global effort to combat climate change [33]. The purpose of this paper is to systematically review the current research of utilizing machine learning technology in enhancing carbon emission monitoring systems in fossil fuel power plants based on the  
Figure 1: The general flow process of machine learning model development [32]. different categories of machine learning methods (e.g., reinforcement learning, artificial neural networks, support vector regression, etc.). The features of each machine learning method are summarized and their advantages and disadvantages are compared. After reviewing the status of current research, challenges and gaps are identified for future research directions. This paper aims to offer insightful perspectives for researchers, power plant operators, and policymakers to empower them to make well-informed choices regarding the integration and advancement of machine learning technologies in monitoring CO2 and NOx emissions from fossil fuel power plants. In this paper, the primary focus is understanding the role of machine learning applications in the prediction and evaluation of CO2 and NOx emissions, which forms the basis for further reducing CO2 and NOx emissions. When machine learning capabilities are well-understood and validated in CO2 and NOx emissions, the possibility of optimizing related control processes in fossil fuel power plants will be possible to further help achieve the goal of reducing greenhouse gases and pollutant emissions eventually.