Predictive Process Monitoring (PPM) in Process Mining (PM) uses predictive analytics to forecast business process progression. A key challenge is suffix prediction, forecasting future event sequences with activity labels, timestamps, and remaining runtime. Current techniques often focus on one-step-ahead predictions, rely on iterative feedback loops for suffix generation, and underutilize payload data. Additionally, many lag behind recent advancements in model architectures, sticking to LSTM-based models. Addressing these gaps, we propose SuTraN, a novel transformer architecture for PPM suffix prediction. SuTraN avoids iterative prediction loops and utilizes all available data, including event features, to forecast entire event suffixes in a single forward pass. Our approach integrates autoregressive suffix generation, data awareness, and seq2seq learning. Experimental results on real-life event logs demonstrate SuTraN’s superior performance in suffix prediction, highlighting its contributions often overlooked in current research.