Aim: To make an Innovative Spam Prediction of spam emails using Machine learning modeling techniques and to evaluate their performance. Materials and Methods: The initiative's main goal is to collect samples from two different groups. The Decision Tree Algorithm belongs to Group-1, whereas the Random Forest Algorithm belongs to Group-2. Both Algorithms will use the same set of sample sizes. The G-Power in the test set will be at 80%. Result: Data is trained in the given model so that Machine learning can function effectively. Emails are used as inputs after the Random Forest Algorithm is applied, which gives us a probabilistic index and determines if the email is spam or not. The Decision Tree Algorithm outperforms the Random Forest Algorithm, and our hypothesis is significant with a significance value of 0.008.Conclusion:  These results were achieved through machine learning models such as Random Forest Algorithm, and Decision Tree Algorithms. In this paper, we have demonstrated that for the spam filtering method the most efficient algorithms are the Decision Tree Algorithm and Random Forest Algorithm given as they have the highest level of accuracy.