Gestational Diabetes (GD) is a health condition that affects pregnant women, characterized by elevated blood sugar levels that can pose serious risks to both the mother and the unborn child. If not identified and managed promptly, GD can lead to complications such as high birth weight, premature birth, and increased likelihood of caesarean delivery. It may also cause long-term health issues like type 2 diabetes for the mother and obesity or glucose intolerance in the child. Traditional methods for diagnosing GD usually involve a one-time glucose tolerance test conducted during the second trimester. However, this single test might not always detect early-stage symptoms or capture variations in blood sugar levels over time, leading to potential delays in diagnosis and treatment. To address this limitation, this project proposes a predictive approach using Long Short-Term Memory (LSTM) networks, a type of deep learning model particularly suited for analyzing sequential data. The LSTM model processes medical data like blood glucose readings and body mass index (BMI) collected over time, enabling it to identify patterns that indicate early signs of GD. By continuously analyzing these trends, the model can provide early alerts and support dynamic monitoring. This proactive system allows for timely interventions, personalized care, and more effective management of GD risks. It shifts the focus from reactive to preventive care, improving maternal and fetal health outcomes through smarter prediction techniques. Ultimately, integrating LSTM-based prediction into prenatal healthcare offers a powerful tool for enhancing early detection and ensuring better health during pregnancy.