Modeling is a core topic across many disciplines. Generally speaking, a model is a simplified representation of some system, which only captures the aspects and characteristics of the original system that are important for a particular purpose. The relative importance of the various aspects depends, of course, on the intended use of the model.
Artificial intelligence and machine learning do both: depend on the use of models and aid in the construction of some specific kinds of models. Supervised learning methods will, for instance, given data, construct a (regression/classification/…) model that predicts the outputs of the original system given its inputs. Knowledge representation methods, in their turn, provide tools for building models based on human knowledge. Some of these approaches, such as certain probabilistic modeling methods are also reasonably proficient at combining human knowledge with data.
To give an instance of how artificial intelligence and machine learning methods may depend on the use of models, one may mention the area of sequential decision making, where model-based reinforcement learning methods rely on the use of models, which they can plan through or otherwise use to achieve better results.