Handling large-scale data is a formidable task for data scientists and researchers due to the rapid generation and ever-growing volume of data. The sources encompass a wide range of current datasets and databases that contain both structured and unstructured data. Hence, the utilisation of advanced algorithms is important to effectively target the vast amount of data before it becomes inaccessible to the current algorithms. Distributed computing has gained popularity as it provides superior scalability and performance compared to Traditional Computing Systems. This paper analyses the structured data with the latest algorithms like Random Forest Classifier, Extra Trees Classifier, Gradient Boosting Regressor, Gradient Boosting Classifier, voting classifier, Federated Learning, Distributed Bagging, Distributed Stochastic Gradient Descent, Communication Efficient Ensemble Learning, Model Parallelism algorithms are applied to datasets in both Distributed Computing and Traditional Environments.