**Abstract**

Adaptive Neuro-Fuzzy Inference System (ANFIS), a popular machine learning model, is introduced in this chapter. ANFIS has a long development history and good agreement on scientific accomplishments. The value of ANFIS has grown dramatically along with the great interest in deep learning. We will examine how machine learning and ANFIS are related. Different methods can be used to implement machine learning models. ANFIS is a Fuzzy Inference System (FIS) that works within the context of adaptive networks. It merges the ideas of Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) into a single framework. This framework can learn to estimate nonlinear functions and operates as a universal estimator. This chapter aimed to investigate the behavior of *D* mesons ratios production cross section (*D*þ*=D***<sup>0</sup>**, *D***<sup>∗</sup>** <sup>þ</sup>*=D***<sup>0</sup>**, *D*<sup>þ</sup> *<sup>s</sup> =D***<sup>0</sup>**, *and D*<sup>þ</sup> *<sup>s</sup> =D*þ), differential production cross section of prompt (*D***<sup>0</sup>**, *D*þ, *D***<sup>∗</sup>** <sup>þ</sup> *and D*<sup>þ</sup> *<sup>s</sup>* mesons) as a function of *PT* in pp collisions at ( ffiffi *s* p = 5.02 and 7 TeV) and predict the behavior for others. The ANFIS model was created through a series of trial-and-error experiments. The ANFIS-based model simulation results perfectly fit the experimental data. When tested with non-training data points, the ANFIS prediction capabilities performed well. The ANFIS offers extensive procedures for high-energy physics modeling.

**Keywords:** adaptive neuro-fuzzy interface system, artificial neural network, deep learning, machine learning, artificial intelligence
