Monitoring concrete filament during the 3D printing process is challenging due to noncontact measurements and the risks associated with physical contact. This study explores the use of image processing techniques to measure filament width and texture after deposition while also monitoring each layer’s texture and irregularities. Real-time data streams, line scans, images, and dimensional parameters are utilized to enable monitoring and facilitate necessary adjustments to critical parameters such as the extrusion rate and nozzle speed by the user. In addition, this paper presents the development of a robust and reliable digital twin that simulates the behavior of 3D printing using sensors. By combining in-situ sensor data, the digital twin identifies layer anomalies in concrete printing during production, leading to a deeper understanding of defects in additive manufacturing parts with higher accuracy. This innovative approach contributes to improved quality control and reliability in the production of 3D concrete structures, addressing the challenges posed by the monitoring and control of the printing process.