<![CDATA[The global need for fuel-efficient coupled with minimizing the environmental impacts of ICEs. This review paper highlights how different ANN methodologies such as backpropagation, recurrent neural networks (RNNs), and long short-term memory (LSTM) networks have been applied to optimize engine calibration, improve fuel efficiency, and minimize emissions across a wide range of fuel blends, including hydrogen-gasoline and ethanol-gasoline mixtures. The research focuses on the application of ANN models to predict performance indicators such as brake thermal efficiency, brake-specific fuel consumption, and emissions, reducing reliance on costly and time-consuming experimental tests. The methodology involved a systematic review of peer-reviewed studies published between 2010 and 2024. Studies were selected based on criteria such as relevance to ICE performance and emission control, use of ANN methodologies, and the availability of experimental or simulation data for validation. involves the use of advanced ANN architectures, including backpropagation, RNNs, and LSTM networks, to establish nonlinear relationships between input parameters such as engine speed, load, and fuel type, and output performance indicators. Findings show that comparison between real model and developed program enhanced from ANN model make a difference prediction capability for engine performance enhanced by at least 10 to 15 % of the traditional modeling. techniques, provide better calibration method of ICEs for better fuel consumption. efficiency and reduced emissions. This present study seeks to establish itself in matters that have not been explored in other papers or researches as follows. integration of Hybrid ANN models, which are better than conventional methods in two major trends, one of which is the improvement of the predictive accuracy and the other is the achievement of increased computational efficiency. It is found that the ANN methodologies presents a strong armory in improving the performance of ICE coupled with lowering of emissions with the possibilities of additions for further enhancements of the technology through the incorporation of other machines use of learning techniques in the future studies.]]>
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