Replanting schedules play a crucial role in maintaining plantation productivity and supporting sustainable forest management. However, evidence on how well past replanting schedules can be explained and predicted using plantation management data and soil characteristics remains limited. This study aims to examine factors associated with scheduled replanting intervals in industrial “Eucalyptus” plantations and to assess the reliability of data-driven predictions. A retrospective analysis was conducted using historical plantation management records to investigate patterns in replanting scheduling and their relationship with stand characteristics and soil conditions. This study serves as a retrospective audit of past scheduling decisions, rather than an assessment of biologically verified readiness or the actual outcomes of the replanting efforts. Machine learning methods were applied to analyse these patterns, and predictive performance was evaluated across various harvest years and compared against simpler predictive approaches. The results indicate that plantation age and specific soil characteristics significantly influence the model's predictions. However, more complex models did not outperform simpler reference approaches; in the 2025 evaluation, they yielded an average prediction error of 27.81 days, compared to 16.15 days. These findings suggest that identifying influential variables does not automatically guarantee predictive reliability for operational planning purposes. This study transparently evaluates past replanting decisions and underscores the importance of assessing predictive reliability before implementing artificial intelligence in plantation management.