Ambient Intelligence (AmI) systems are intended to support responsive and adaptive services by understanding the context of their users. The present paper proposes a machine learning framework for context prediction in ubiquitous environments on the basis of multi-sensor ambient data. The method classifies user activity into four high-level contexts: Sleeping, Working, Cooking, and Outdoors, exploiting features like Temperature, Light, Sound, Motion, and Time. The model described here consists of three steps: Preprocessing, Normalization, and Classification based on Random Forests. Visualization techniques, such as KDE plots, correlation matrices, and 3D scatter analysis, are used for the interpretation of contextual separability and the influence of features. Experimental results show that outdoor activity strongly dominates the dataset, while other contexts are almost neglected. The proposed framework shows high predictive accuracy with interpretable insights into context-dependent behavioral patterns. This work thus contributes to the design of such adaptive, context-aware systems that can find a home in intelligent environments, especially in the realms of smart homes and assistive technologies.