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This dataset contains project management data extracted from Jira, used in the research “Predictive Analysis Using Machine Learning with CRISP-DM and Random Forest for Software Project Delivery Delay Mitigation in an HRIS SaaS Company.” The dataset includes historical epic-level project records from 2024, covering attributes such as project identifiers, issue identifiers, status and workflow information, time and effort metrics, team and resource attributes, and quality indicators. Data preprocessing involved handling missing values, outlier treatment, data type conversion, and feature engineering (including time-based metrics, effort ratios, aggregated project-level features, and textual features). The dataset is intended for use in predictive analytics research, specifically focusing on project delay prediction using machine learning models. It supports experimentation with techniques such as Random Forest, XGBoost, and other classification algorithms, and can be used to reproduce or extend the results presented in the associated research paper.