The predictive maintenance (PdM) has become a predominant gaming play for minimizing the downtime and resources utilization as well as for sustaining equipment reliability in the industrial settings. However, a numerous amount of the existed PdM methodologies have not been well- integrated with SCADA systems, real-time enough and not explainable a line with the human operators. This paper introduces a new AI-Driven Predictive Maintenance Framework for SCADA-based industrial plants. Utilizing real-time SCADA data feeds, machine learning algorithms as well as an XAI standard, the architecture is able to predict faults early, provide transparency in decision support, and schedule dynamic maintenance. As opposed to previous works, the proposed solution can be natively included in SCADA infrastructures providing a transparent interaction with the hierarchy control layers by adding cyber security-aware schemes for anomaly detection. The scalable solution enables customization for variety of industries like manufacturing, power generation and process industries. In addition, the framework promotes Industry 4.0 change by providing continuous loops between physical equipment and model intelligence opening the possibilities of integrating digital twins. Experimental verification shows that the proposed PdM framework results in efficient fault detection, improved operational efficiency, and cost savings, when compared to the current PdM approaches, with enhanced system transparency, safety, and sustainability.