Cloud computing serves as a foundational infrastructure for modern digital enterprises, enabling on-demand access to data and applications through scalable resources. This research addresses the challenges of predictive analytics in cloud environments, where traditional classification methods often struggle with high time complexity and limited accuracy. The methodology involves a systematic process of data collection from Internet of Things (IoT) devices, followed by noise-reducing pre-processing and feature selection for dimensionality reduction. The study evaluates various machine learning and deep learning models, including XGBoost, Bi-LSTM, and Light GBM, using two distinct datasets: Smart Farming Data 2024 (SF24) and a comprehensive healthcare dataset. Major findings indicate that for agricultural applications, a cluster-based classification method achieved a 88.25% prediction accuracy with a significant 39% reduction in prediction time. In healthcare scenarios, a Light GBM and KNN-based ensemble algorithm attained a superior accuracy of 89.66%. The study concludes that machine learning-based classification provides a robust framework for efficient predictive analytics, significantly outperforming conventional methods in both precision and computational efficiency.