###### Abstract  
Fossil fuel power plants are a significant contributor to global carbon dioxide (CO\({}_{2}\)) and nitrogen oxide (NO\({}_{\rm x}\)) emissions. Accurate monitoring and effective reduction of these emissions are crucial for mitigating climate change. This systematic review examines the current state of research on the application of machine learning techniques in evaluating the emissions from fossil fuel power plants. This review first briefly introduces the continuous emission monitoring (CEM) systems and predictive emission monitoring (PEM) systems that are commonly used in power plants and highlights that machine learning models can significantly improve PEM systems through their capability to process and interpret large datasets intelligently to transform traditional emission monitoring systems by enhancing their precision, effectiveness, and cost-efficiency. Compared to previously published review articles, the key contribution and innovation in this present review is the discussion of machine learning models in CO\({}_{2}\)/NO\({}_{\rm x}\) emissions according to the different algorithms used, including their advantages and disadvantages in a systematic way, which aims to help future researches to develop more effective machine learning models. The most popular machine learning model includes reinforcement learning, a forward neural network, a long short-term memory neural network, and support vector regression. While each model method has its own advantages and disadvantages, we noted that training data quality, as well as the proper selection of model parameters, plays an important role. The challenges and research gaps, such as model transferability, a deep understanding of the physics of CO\({}_{2}\)/NO\({}_{\rm x}\) emissions, and the availability of high-quality data for training machine learning models, are identified, and recommendations as well as potential future research directions to address these challenges are proposed and discussed. artificial intelligence; carbon dioxide gas; nitrogen oxides; predictive model +
Footnote †: journal: Applied Sciences  
(O\({}_{2}\)) and is normally formed under high temperatures in the combustion process. There are some common ways to help reduce the emission of NO\({}_{\rm x}\), including gas recirculation, reduced air preheating, and a reduced firing rate [3]. The reduction of anthropogenic CO\({}_{2}\)/NO\({}_{\rm x}\) emissions can effectively reduce the trend of climate change, as evidenced by the abundance of reviews in the literature [4; 5; 6]. In 2021, global CO\({}_{2}\) emissions increased by 6% to the highest-ever level of 36.3 billion tons [7]. Based on the most recent data from the U.S. Energy Information Administration, approximately 60% of the electricity generation was from fossil fuels--coal, natural gas, petroleum, and other gases [8]. Furthermore, the emission of greenhouse gases, such as CO\({}_{2}\), from developing countries is rapidly increasing along with their economic growth [9; 10; 11]. It is estimated that approximately 28% of global total energy consumption will be from fossil fuels by 2040 [12]. Although renewable energy is gaining momentum as an alternate energy source, it is widely reported that fossil fuels are still primarily used to meet the ever-growing energy consumption requirement in the world [9; 13; 14]. Aside from CO\({}_{2}\) emissions, NO\({}_{\rm x}\), which is mostly emitted from the combustion process in fossil fuel power plants, also contributes to global climate change. In addition, it has numerous hazardous effects on human beings, vegetation, and animals [15; 16; 17]. The emission of CO\({}_{2}\) mostly depends on the amount of fossil fuels consumed, while NO\({}_{\rm x}\) is commonly generated under high temperatures and pressures [18]. Furthermore, the consumption of fossil fuels produces not only greenhouse gases but also other pollutants that are harmful to human health and the environment [19].