Intelligent IT support systems based on statistical log analysis and machine learning models

Modern IT environments generate high-volume operational logs that are critical for incident detection, diagnosis, and service restoration. However, manual interpretation of heterogeneous logs and unstructured service desk tickets often leads to alert fatigue, delayed triage, and inconsistent routing decisions. This paper presents an intelligent IT support system that integrates statistical log analysis with machine learning models to improve end-to-end incident handling. The proposed approach treats log-derived measurements as a statistical evidence stream and applies a variance-normalized, constraint-aware inverse model to estimate interpretable incident-component proportions and an explicit fit score that reflects how well observed behavior matches known incident patterns. These statistically grounded outputs are then used as structured features for machine learning models that automate IT support actions, including incident categorization, priority estimation, and assignment-group routing. Using real organizational operational data, the system is evaluated on detection quality, routing performance, and workflow outcomes such as alert volume and time-to-assignment. Results indicate that the statistical layer improves interpretability and governance by separating well-explained incidents from low-fit/novel cases, while the hybrid statistical-ML design improves support decision quality compared to text-only approaches. The study demonstrates that combining statistically defensible evidence with learning-based automation can reduce operational overhead and strengthen trust in intelligent IT support systems.