This study presents an analytical framework, employing artificial neural networks (ANNs), to characterize the fresh properties of self-compacting mortar (SCM) reinforced with 6mm long carbon fibers at varying content ratios (0.05, 0.1, 0.2, and 0.4% by weight). Utilizing a multi-layered network approach with reactive error variance, the analysis delivers a nuanced understanding of the influence carbon fibers exert on SCM's fresh characteristics. It was observed that the inclusion of carbon fibers extended the passage time through a mini funnel and contracted the flow diameter, indicating alterations in workability. The ANN model demonstrated a high degree of predictive accuracy for fresh SCM properties, achieving a validity of 97.5% and a coefficient of determination (R 2 ) of 89.4%.