<p>Self-compacting mortars and concretes, commonly used in horizontal structures, exhibit superior fluidity and homogeneity. However, these materials are prone to significant shrinkage and cracking, especially in floors and slabs due to large moisture exchange surfaces. Incorporating fibres offers an efficient alternative to traditional reinforcement methods by reducing shrinkage-induced cracking. This study evaluates the effectiveness of glass fibres, polypropylene mono-filament, and polypropylene multi-filament fibres in controlling cracking phenomena. Three-point bending tests with acoustic emission (AE) monitoring were conducted to investigate different stages of cracking mechanisms. The results indicate that while fibre addition does not significantly affect the maximum force, fibre dosage and type substantially influence post-peak mechanical behaviour, particularly the residual force and toughness index. In order to identify the source failure mechanisms, acoustic emission signals were analysed. Two types of investigations are performed: AE signature investigation and AE multi-parametric investigation. Two clustering methods: Multivariable K means non supervised and KNN supervised machine learning methods are applied to identify the contribution of each failure mechanism namely; fibre breakage, fibre-matrix sliding, and matrix cracking. Waveform analysis further revealed characteristic AE signatures for each mechanism, highlighting the potential of AE for understanding and optimizing fibre-reinforced mortar’s crack resistance. The combination of AE and machine learning clustering methods. The supervised KNN model achieved a high classification performance in terms of distinguishing the mechanisms confirming the reliability of the combined AE–machine learning approach.</p>