This paper provides a comprehensive review of smart meter data analytics, focusing on applications, methodologies, and challenges. Smart meters have become widely adopted, enabling the collection of fine-grained electricity consumption data. The deregulation of the power industry has driven the need to leverage this data to enhance grid efficiency and sustainability. The paper identifies key application areas: load analysis, load forecasting, and load management. It reviews techniques such as time series analysis, dimensionality reduction, clustering, classification, and deep learning. The paper also discusses emerging trends like big data issues, novel machine learning technologies, new business models, energy system transitions, and data privacy and security. A bibliometric analysis reveals a rapid increase in publications since 2012, with IEEE Transactions on Smart Grid being the most popular journal. The paper highlights the importance of bad data detection, energy theft detection, and load profiling for accurate load analysis. For load forecasting, it reviews both point and probabilistic methods, emphasizing the use of smart meter data to improve accuracy at various levels. The paper concludes by identifying open research questions and future directions, including the need for ensemble detection frameworks, distributed clustering methods, and more effective feature extraction techniques.This paper provides a comprehensive review of smart meter data analytics, focusing on applications, methodologies, and challenges. Smart meters have become widely adopted, enabling the collection of fine-grained electricity consumption data. The deregulation of the power industry has driven the need to leverage this data to enhance grid efficiency and sustainability. The paper identifies key application areas: load analysis, load forecasting, and load management. It reviews techniques such as time series analysis, dimensionality reduction, clustering, classification, and deep learning. The paper also discusses emerging trends like big data issues, novel machine learning technologies, new business models, energy system transitions, and data privacy and security. A bibliometric analysis reveals a rapid increase in publications since 2012, with IEEE Transactions on Smart Grid being the most popular journal. The paper highlights the importance of bad data detection, energy theft detection, and load profiling for accurate load analysis. For load forecasting, it reviews both point and probabilistic methods, emphasizing the use of smart meter data to improve accuracy at various levels. The paper concludes by identifying open research questions and future directions, including the need for ensemble detection frameworks, distributed clustering methods, and more effective feature extraction techniques.