Industrial maintenance ensures the reliability, efficiency, and safety of manufacturing systems. Yet, many industries still depend on paper-based checklists, manual reporting, and visual inspections-causing inefficiencies, delayed fault detection, and redundant documentation during audits. This paper presents FixFlo, a digitalized framework that integrates machine learning-based fault prediction with automated maintenance log management. It structures data such as machine specifications, preventive checklists, breakdown reports, and reliability metrics (MTTR, MTBF), and processes them using Random Forest, XGBoost, and SVM to predict faults and detect anomalies. By replacing paper workflows with a centralized digital system, FixFlo reduces human error, enables proactive maintenance, and simplifies compliance reporting. It offers a scalable, cost-effective step toward Industry 4.0, enhancing reliability management across industries.