Physics-Informed Machine Learning (PIML) has emerged as an innovative approach that integrates previous knowledge from physics into machine learning models, improving their ability to solve engineering problems. Within the PIML framework, Physics-Informed Neural Networks (PINNs) have gained importance due to their ability to enforce physical laws as constraints directly during the training process, reducing the need for large and labeled datasets. This thesis begins with a theoretical exploration of PIML, distinguishing PINNs as a specific class of physics-informed machine learning models. We then analyze the core principles and key challenges of PINNs, including simple applications of the framework to illustrate its foundational concepts.