In the framework of Industry 5.0, in which the focus resides on fusing the latest innovations with ecologically sound and human-centered practices, predictive maintenance has become a vital component of manufacturing operations. To improve efficiency in operations, minimize downtime, and reduce waste—all of which are in perfect harmony with the green practices that are being promoted in this day and age—it is essential to be able for prediction various components failures as well as organize maintenance schedules. To plan and make predictive and corrective maintenance successful of mechanized systems, this chapter focuses and examines use of deep learning (DL) approaches for health analyzing and estimating remaining useful life (RUL) of turbofan engines. To evaluate the efficacy of various deep learning techniques in analyzing sensor-based time-series data, a comparative study is carried out using various deep learning techniques. Results from this comparative analysis provide useful information in choosing the best DL techniques for turbofan engine RUL prediction. By using these strategies, predictive maintenance can be greatly improved, guaranteeing prompt interventions, longer asset lifespans, and compliance with Industry 5.0 guidelines. The chapter emphasizes how crucial it is to combine human knowledge with cutting-edge technology to develop systems that are not only effective but also long-lasting and flexible enough for meeting the changing needs.