Monitoring the structural health of civil infrastructures has become an essential aspect of modern engineering to ensure safety, durability, and cost-effectiveness. Traditional methods of structural inspection are labour-intensive and often unable to detect early-stage damage or predict future distress and failures. Integrating embedded sensors within the concrete offers a promising solution for continuously monitoring the health of the structures, providing real-time data for detecting damage at its inception. So, this paper tells about the scope of sensor embedding in concrete and the role of artificial intelligence (AI) in structural health monitoring (SHM). A method is used to embed sensors in concrete, and an AI technique is used, in which different types of sensors are used, such as strain gauges, temperature and humidity sensors, and load cells. These sensors are to be placed in a concrete methodical way to measure strain, temperature, humidity, and load. Then, a hybrid model will be made to predict the structural health over time. The deep learning models, such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), from which CNN will extract features from spatial data and RNN will understand the sequential data, meaning time series data, which will predict the crack formation and structural issues. This hybrid model will effectively detect the micro-crack inside concrete and provide an early sign of degradation of the building with an accuracy of 90–95%. Also, this system will give us durability during various environmental conditions like changes in temperature, humidity and loads. The early detection ability of the system will reduce the cost of maintenance and also help extend the life of the building. This system will identify the damage before it becomes critical. This paper will highlight the ability of sensors embedded in concrete and AI used in SHM practices. It is cost-effective, a good alternative to traditional and manual inspection methods, and a time-saving and innovative strategy. Further, the study will give us an understanding of developing new and innovative ideas, which will generate real-time data for building maintenance, safety, and decision-making.