Artificial Neural Networks (ANN) are widely recognized as a viable technique for addressing complex and uncertain problems. They are not programmed in the traditional sense but instead trained on historical data representing a system’s behavior. ANN is used in a variety of applications in industries, including construction. The construction process is highly unpredictable due to the many unknowns that construction professionals must deal with, such as safety, quality, cost, and time. By reviewing previous literature, studies, and theories, this study aims to investigate the feasibility and application of ANN in evaluating the construction sector’s capacity. The methodological approach used in this study is a mixed methodology based on a literature review, a bibliometric analysis of emerging trends, and a conceptual framework. The literature review investigates the applicability of ANN in the construction industry by reviewing studies on energy consumption, material optimization, quality control, and safety, among other topics. Various types of ANN models, such as Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Self-Organizing Maps (SOM), are investigated, as well as how each of these models can be used to address various challenges in the construction industry. The construction-related data was suggested in an approach to develop the conceptual framework to predict the usability of the neural network.