The more integrated the modern SAP ERP system becomes, the greater the challenge of ensuring the accuracy, interpretability, and real-time detection of anomalies. Escalating complexities, business logics, and the dynamic nature of crossfunctional modules make it ever more necessary to provide seamless explanatory systems that root explain causative analyses of various statistical and machine learning features employed. In this work, a new deep learning hybrid neural-symbolic approach is introduced to detect anomalies and automate root cause analysis in sap ERP systems. This architecture combines semantic rulebased models with deep neural frameworks to not only pinpoint anomalies within diverse operational, financial, and material data but also map their causal links within SAP modules and extend their reach beyond inter-ERP systems. A comprehensive evaluation on a hybrid dataset containing authentic SAP log files and simulated anomalies was conducted, proving remarkable accuracy in anomaly detection alongside improved clarity and transparency in root cause analysis when benchmarked against existing methods. The experiments revealed a 21% improvement in F1 score alongside 35% improvement on mean time to root cause detection. The work sets a new standard of agility and intelligence diagnostic frameworks on SAP-based enterprises intending to curb operational risks, ensure compliance, and promote improved organizational decision-making latency and efficiency.