The rapid evolution of urban mobility and smart city demands an intelligent transportation system which can make real-time decisions using lightweight and adaptive AI models. This research introduces a novel application of tiny machine learning which will combine the features of Few-shot learning algorithm and it will classify the traffic intensity levels on regional traffic data. By converting the traffic volume into three dynamic classes (Low/ Medium/ High), a compact neural network model is trained on episodic few-shot tasks that can mimic real-world low-data learning conditions. The proposed work supports open set classification which is more suitable for detecting unknown traffic behavior analysis by considering the previous day traffic level and how the future traffic intensity level can be predicted effectively. The accuracy of the proposed method is compared with the existing methods which lie with the baseline CNN (90 %) and SVM (89 %). But the average episode accuracy achieved through the proposed model is 95.2 % which makes this model promising for low-power edge deployment in intelligent transportation system.

The Satellite-Terrestrial Integrated Networks (STIN) were emerged as a key architectural pattern for attaining seamless, global and resilient wireless connectivity by adding extensive coverage of satellite systems with the high capacity and low latency of terrestrial networks. In spite of their advantages, STINs face significant challenges arising from heterogeneous link qualities, dynamic network topologies, long propagation delay and highly variable channel conditions which may complicate reliable and adaptable communication. The accurate and timely assessment of link quality is essential to enable effective resource management, adaptive modulation and coding and robust network control in space-ground integrated environments. In this study, AI-based link quality classification model for STINs based on a stacked ensemble learning architecture. This model combines multiple lightweight machine learning classifiers and a meta-level learner to capture complex non-linear relationships among satellite orbital parameters, spatial characteristics, and link dynamics. The framework categorizes satellite-terrestrial links into three operational states as Good, Moderate and Poor, which provides actionable intelligence for cross-layer resource allocation and adaptive communication strategies. The extensive experimental evaluation demonstrates that the proposed work attains 96.79% classification accuracy and 96.88% macro averaged F1-score where Decision tree as 85% and Machine Learning Based attain 88%. This indicates highly balanced and robust performance across all link classes. The confusion matrix analysis reveals that the misclassification occurs only between adjacent link quality states with no critical misclassifications between good and poor links. This ensures high reliability for operational decision-making. When compared to single-model baselines, the proposed approach increases prediction stability and robustness under heterogeneous and dynamic STIN conditions. The results confirmed the machine learning-assisted link quality intelligence can serve as a practical and efficient enabler for dynamic resource management in STIN. The model is computationally effective, scalable and readily deployable within next-generation STIN control planes which supports reliable communication for broadband access, emergency services and IoT applications.