The increasing complexity of career decision-making for economics students necessitates innovative approaches to provide effective guidance. This chapter explores the application of machine learning (ML) and deep learning (DL) techniques in predicting and recommending suitable career paths for students. Leveraging a diverse dataset comprising academic performance, skills, interests, and career aspirations, we implemented various ML algorithms, including K-Nearest Neighbors (KNNs), Decision Trees, Random Forests, Support Vector Machines (SVMs), and Naive Bayes, alongside deep learning models such as Deep Neural Networks (DNNs) and Long Short-Term Memory (LSTM) networks. Our methodology involved rigorous data preprocessing, model training, and performance evaluation using metrics like accuracy, precision, recall, and F1 score. The findings reveal that DL models, particularly LSTM networks, outperform traditional ML algorithms in capturing complex patterns within the data, leading to more accurate career predictions. The developed recommendation system provides personalized career guidance, which has been validated through initial user feedback from students. This chapter highlights the potential of advanced predictive analytics in enhancing career counseling services and suggests pathways for future research and system improvements.