Aim: The aim of the work is to evaluate the accuracy in predicting pancreatic cancer using Artificial Neural Network(ANN) and Decision Tree Algorithms. Materials and Methods: In this work there are two groups in which each group has 20 sample sizes and total sample size is found to be 40, with pre-test power of 80% (G-power), α=0.05, confidence interval 95%. The effectiveness in identifying the pancreatic cancer by the algorithms is evaluated. Results: It has been observed that the artificial neural network is much better than the decision tree algorithm in terms of accuracy in predicting the cancer. Each algorithm gives different accuracies where ANN has better mean accuracy of 90.06% which is better than the decision tree algorithm 85.9%. The statistical results are also provided where Artificial Neural Network and Decision tree algorithms have statistical significance different values i.e. p<0.01 (independent sample T-test). Conclusion: The results show that the proposed artificial neural network is efficient when compared to decision tree algorithms