Recruiters and hiring managers often face challenges in efficiently evaluating large volumes of job applications. Traditional manual screening methods are time-consuming and prone to bias, leading to suboptimal hiring decisions. This research presents an automated approach for job application evaluation using Natural Language Processing (NLP) techniques and machine learning regression models. The methodology involves data preprocessing, feature extraction using techniques like TF-IDF, Word2Vec, and Latent Dirichlet Allocation, and sentiment analysis for candidate profiling. Regression models, including Random Forest, Support Vector Regression, and LightGBM, are applied for predictive scoring. The results indicate that text similarity metrics, particularly cosine similarity, yield the highest correlation with job matching scores, while ensemble learning models, especially LightGBM, demonstrate superior predictive accuracy with an MSE of 0.0096 and an \(\textrm{R}^{2}\) value of 0.6358. The findings suggest that optimizing feature selection and employing advanced ensemble learning techniques significantly enhance job application screening. The impact of this research is substantial, as it provides an efficient, unbiased, and scalable approach to CV screening, reducing recruiter workload and improving the precision of candidate selection.