Pancreatic cancer is a highly lethal disease that often goes undetected until it reaches an advanced stage. Analyzing the criticalness of pancreatic cancer using machine learning can provide valuable insights into the factors that contribute to the disease and help in the development of effective prevention and treatment strategies. This study focuses on using machine learning techniques to predict the criticalness of pancreatic cancer based on various clinical and demographic variables. The study involves data preprocessing, feature selection, and model development using machine learning algorithms such as logistic regression, decision trees, support vector machines, neural networks, and deep learning. The developed machine learning models achieved high accuracy in predicting the criticalness of pancreatic cancer, indicating their potential in supporting clinical decision-making and personalized treatment planning. This study highlights the importance of machine learning in the early detection and prevention of pancreatic cancer, ultimately leading to better patient outcomes. Overall the neural network model shown promising results in terms of accuracy as 95%, precision as 95%, recall score as 90% and F1 score as 90%.