Smart Data frameworks represent the next evolution in data analytics, transforming raw, high-volume Big Data into actionable insights that emphasize accuracy, agility, and actionability. This review systematically explores the methodologies underpinning Smart Data systems, focusing on five critical components: data acquisition and storage, preprocessing, analytical models, real-time analytics, and the extraction of actionable insights. The discussion highlights advanced preprocessing techniques such as ensemble noise filtering, synthetic minority oversampling, and dimensionality reduction methods like PCA and t-SNE. It examines analytical models that encompass decision tree ensembles, Bayesian inference, and deep learning architectures, all of which provide robust and scalable insights across diverse applications. Real-time analytics frameworks, including Apache Flink and Structured Streaming, are considered for their ability to support low-latency processing, while IoT-specific protocols such as MQTT address resource constraints in distributed environments. Despite these advancements, challenges persist, including issues related to scalability, the integration of batch and real-time workflows, and the absence of unified toolchains. Future directions emphasize hybrid analytics frameworks, automated preprocessing pipelines, domain-specific Smart Data systems, and the incorporation of explainable AI. By addressing these challenges and leveraging emerging opportunities, Smart Data frameworks have the potential to revolutionize industries ranging from healthcare to the IoT, enabling efficient, real-time decision-making and unlocking the full potential of data-driven systems.