With the advent of Industry 4.0, predictive maintenance (PdM) has become a major paradigm in ensuring that the machine faces minimum time under maintenance, risking fewer operational costs as well as a boost in the assets. Nevertheless, the implementation of efficient PdM applications in a wide range of the manufacturing settings is still a daunting task, as the heterogeneity of the considered systems, the variability of sensor setups, and the scarcity of labeled failure data are issues. The paper discusses how transfer learning (TL) can be used as an effective solution to the performance mismatch present in low-resource and problematic heterogeneous manufacturing systems. TL allows taking advantage of knowledge through data-rich source domain and imparting it to data-limited target domains, minimizing the reliance on mass labeled data and maximizing cross-platform model generalization. The study proposes a TL framework that utilizes deep learning methods to optimize cross-domain predictive maintenance that are optimized using techniques in fine-tuning, domain adaptation techniques, and transformations in features. The datasets used to perform experimental evaluations are provided by several industrial systems of different types of machines and operation patterns. The findings indicate a high level of accuracy in predicting the outcome, stability of the model and flexibility that is higher as compared to when using the traditional machine learning techniques, which are trained in isolation. The model TL suggested is capable of delivering good results in the case of limited labeled target data, therefore, being useful to small- and medium-sized manufacturers. Significant challenges associated with data heterogeneity and variability in a system have also been covered in this article involving the use of preprocessing approaches and domain-specific normalization procedures. Confusion matrices, bar charts, and pie charts are used to help determine model effectiveness as visualizations. Finally, the research also advances the industrial AI literature by proposing an efficient, generalizable, and scalable method of predictive maintenance. The results recommend the broader application of transfer learning to real-life manufacturing settings and provide notions of a potential future research, including the addition of federated learning and real-time data adaptation.