The purpose of this research is to analyze the efficacy of modern machine learning (ML) algorithms as well as methods for personality prediction from resumes. Specifically, the study compared the conventional random forest (RF) algorithm approach with logistic regression (LR) in an effort to improve the accuracy value. Two sets of forty samples each were used for this study, for a total of eighty samples. While Group 2 employs a LR technique, Group 1 employs a unique RF model. As part of the research process, the dataset was loaded into Kaggle and trained utilizing Jupiter Notebook and the new RF technique. This online analytical application takes into account the characteristics obtained from previous studies and determines the sampling size with an alpha level of 0.430 as well as 95% pretest efficiency. While LR achieves an accuracy of 86.6340%, the innovative RF technique achieves a 90.9700% accuracy, as seen in the simulation results. With values of 0.014 (P < 0.05), the methods significantly vary in accuracy. The offered dataset is suitable for both LR and Novel RF, two excellent ML methods; nevertheless, the Novel RF method outperforms the other when it comes to predicting personality traits from resumes.