The ever-increasing implementation of IoT sensor networks in smart environments has created a multitude of multivariate spatio-temporal data streams. The effective identification of temporal patterns and anomalies in those streams becomes necessary to ensure environmental awareness and operational integrity. This paper proposes an elaborate framework for temporal pattern recognition in IoT sensor data through the integration of spatio-temporal reasoning, statistical modeling, and anomaly detection. The approach uses statistical descriptors, kernel density estimation, correlation matrices, and spatio-temporal visualization to capture local anomalies as well as trends prevalent in aggregation over distributed sensors in order to illustrate its strengths in analyzing the data. One of the case studies depicts the environmental awareness based on monitoring temperature: deviation detection, inter-sensor coherence, and a conceptual view of anomaly propagation. The results indicate the potential of establishing a system capable of real-time and interpretable monitoring in dynamically evolving IoT settings. This work contributes toward more adaptive and transparent sensing architectures capable of operating under uncertainty and environmental variability. The abstract is coherently structured, flowing from motivation to case study, methodology, results, and contribution. It also accurately frames the rationale for the study and successfully defines the fundamental difficulty in IoT sensor networks, which is the recognition of temporal patterns and abnormalities.