Over recent years, the significance of ML for predictive systems in different fields such as healthcare, finance, and customer care, has exponentially expanded. Problems such as data sparsity, the need for manual feature engineering, and the requirement for model retraining in new specific domains persist despite the prevalence of machine learning models like Random Forest, SVM, Gradient Boosting, and XGBoost, which are working fine. To manage these restrictions, many handling usually need on-going human interface, which can hamper scalability, especially when the new data is arising. That being said, this paper focuses on incorporating Augmented Data into standard Machine Learning Models to overcome these obstacles. Because such techniques create artificial data, these models can enrich training sets, which is particularly helpful in fields where data is scarce. An experiment with a concrete example is made with a real user behaviour phone usage dataset, generating synthetic data and mixing it with real data from which the Data Augmentation is made through various Augmentation Techniques. The performance of models evaluated on real and synthetic data sources is compared for models built with both the isolated set and integrated approaches, showing an average 9% improvement. This type of hybrid model improves the forecast and lowers the dependence on manual data gathering, making machine learning solutions more effective and efficient.