Concrete walls are critical structural elements subjected to tension, compression, shear, and bending moments. When employed as compression walls, the primary stresses are tension and compression. Over time, the materials comprising these walls can undergo aging, leading to reduced performance. A commonly used technique to extend the lifespan of concrete walls is reinforcing them with carbon fiber composites. These composites are bonded to one or both faces of the wall to enhance its resistance. This paper focuses on assessing the resistance of single-faced and double-faced reinforced concrete walls using artificial intelligence (AI). A model was developed to generate a comprehensive database of wall configurations and corresponding structural responses. This database was used to train an AI algorithm, which was then validated by comparing its predictions to real-case scenarios. The trained AI model demonstrated the ability to predict the resistance of wall configurations not included in the training dataset. The results highlight the predictive accuracy and potential of AI in optimizing the design and reinforcement of concrete walls.