In the current scenario, the recommendation system is challenging to maintain due to three key requirements: the need for real-time user behavior analysis, the inability to explain why recommendations are made, and struggles to handle new users/items. In this article, our objective is to develop a hybrid recommendation system that solves the challenges of traditional approaaches. Our framework combined real-time learning, agentic rules, as well as sensor compatibility in a dynamic environment. We develop a novel framework called SAFIRE (Sensor-Aware Framework for Intelligent Recommendations and Explainable Hybrid Techniques), where the 8 traditional algorithms (User-Based CF, Item-Based CF, KNNWithMeans, KNNBaseline, SVD, SVD++, NMF, BaselineOnly), a Hybrid ensemble, and Explainable AI are used to recommend it. From our experimental work, it reveals that the accuracy of BaselineOnly provides an RMSE score of 5-fold RMSE of 0.5156, and MAE is 0.34055. Similarly, 10-fold CV of RMSE is 0.51558, and MAE is 0.34069. The lowest MAE of the 5-fold is 0.29913. For 10-fold, NMF MAE is 0.30144. This study also conducted the statistical test and found that Memory-Based CF (KNN variants, UserCF, ItemCF), having 10-fold CV, performs slightly better than 5-fold., p-values are significant.NMF, the mean difference is −0.00248 very small improvement in 10-fold CV, and p-values < 0.05, which is significant. Model-based techniques like BaselineOnly, NMF, and SVD show little variation (mean difference < 0.003) and hold up well during CV folds.