By converting high-level user objectives into workable configurations, intent-based networking, or IBN, seeks to automate network administration. Predicting future intentions to facilitate proactive resource management is a major difficulty in IBN. The use of Long Short-Term Memory (LSTM) networks for network intent prediction is examined in this research. Using the Network Intent Language (NILE), we create a synthetic dataset of 8,353 intents and assess how well the LSTM model performs across a range of temporal window sizes. Our findings show that an LSTM model with a medium window size (e.g., 12) may successfully strike a compromise between stability and responsiveness, which makes it appropriate for real-world intent forecasting tasks like resource allocation in 5G networks and proactive service orchestration.