Predictive maintenance (PdM) is an integral part of any modern industry. It aims to predict failure and maintenance schedules for key components across the industry using historical and current sensor data, leveraging Artificial Intelligence (AI). However, the costs associated with industrial maintenance exhibit extreme asymmetry, with the economic penalty for a False Negative (missed failure) significantly higher than that for a False Positive (nuisance alarm). Furthermore, existing cost-sensitive boosting methods often struggle with mixed objectives, where misclassification costs are directly injected into the training phase (e.g., through gradient weighting). Subsequently, these models generate noisy alarm profiles that are difficult to tune to asset-specific risk tolerances. Therefore, there is a critical need for a framework that preserves ranking integrity while also allowing precise, decoupled cost optimization at deployment time. Hence, this paper proposes a Cost-Sensitive Thresholding-based Light Gradient Boosting Machine (Decoupled), abbreviated as (CST-LGBM-D) model, which separates cost-sensitive decision optimization from model training for reliable PdM. The proposed CST-LGBM-D model offers accuracy of 94.00%. The comprehensive experimental analysis on the benchmarked dataset supported by explainable AI validates the superiority of the proposed CST-LGBM-D model.