Remaining Useful Life (RUL) prediction is crucial for prognostics and health management (PHM) in industrial applications, as it helps to reduce unexpected maintenance and downtime costs. This study introduces AttNet, an Attention-Based Bidirectional Gated Recurrent Unit(BiGRU) Network designed for RUL prediction that can effectively captures and prioritizes key temporal features in the data, leading to more accurate RUL predictions. Our model builds upon previous works, specifically improving upon the approaches proposed by Z. Chen et al. [1] and Y. Zhang et al. [2] both of which utilized deep learning models for RUL prediction on the NASA C-MAPSS turbofan engine dataset. Experimental results show that AttNet outper- forms these studies, achieving an RMSE of 12.02 on FD001 (a 17.27% improvement over Z. Chen et al. [1] and 9.07% better than Zhang et al. [2] and 12.54 on FD003 (a 9.06% improvement over [2]). Experimental results shows the effectiveness of our Att- Net architecture in accurately predicting RUL.