The acoustic emission (AE) technique has proven to be an effective method for detecting individual wire breaks in prestressed concrete structures affected by stress corrosion cracking (SCC). However, despite its widespread application, the quantitative assessment of AE monitoring reliability and optimal sensor placement remains unresolved. This study introduces a statistically and experimentally grounded framework, informed by physical mechanisms of signal propagation, for determining reliable sensor spacing in AE monitoring systems. A comprehensive experimental database of wire breaks and long-term bridge monitoring data were analyzed to develop probabilistic models for source characteristics, attenuation, and traffic- and environment-induced noise. These models were integrated within established probability of detection (POD) and probability of false alarm (PFA) frameworks. The POD analysis links detection reliability with sensor network design metrics, while the PFA introduces a localization-based alarm criterion that improves interpretability for structural assessment. Results show that reliable detection is achieved at source–sensor distances between approximately 8 and 13 m, depending on selected signal feature and structural conditions. Signal energy proved to be a more robust indicator than peak amplitude, allowing up to 25% greater sensor spacing. The proposed approach provides a transparent and experimentally accessible methodology for quantifying the reliability of AE detection and designing sensor networks, thereby enhancing the interpretability of AE-based structural assessments.