aspg-18-744-1792025483 20261015005123 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2021 3 1 Navod Neranjan Thilakarathne University of Colombo, SRILANKA Rohan Samarasinghe University of Colombo, SRILANKA Mohan Krishna Kagita Charles Sturt University, AUSTRALIA Surekha Lanka Stamford international university, THAILAND Hussain Ahmad Gomal University, PAKISTAN; In the 21st century, the Smart Grid (SG), also known as the next-generation power grid, arose as a substitute for inefficient power systems, ensuring a reliable and efficient power supply. It is projected to improve the reliability and efficiency of energy distribution while having minimal side effects because it is coupled with modern communication and computation capabilities. The huge infrastructure it possesses, as well as the system's underlying communication network, has resulted in a large number of data that necessitates the use of diverse approaches for proper analysis and decision making. When it comes to analyzing this huge amount of data and generating significant insights from it, big data analytics, machine learning (ML), and deep learning (DL), all play a key role. These insights are useful for anomaly detection, fraud detection, price confirmation, fault detection, monitoring energy consumption, and so on. Hence constant and continuous data analysis is an essential part, of the modern smart grid, for its existence. Inspired by providing a reliable and efficient energy distribution, this paper explores and surveys the smart grid architectural elements, ML and DL based applications, and approaches in the context of SG. In addition in terms of ML and DL based data analytics, this paper highlights the limitations of the current research and, highlights future directions as well. 2021 32 42 744 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.030103 https://americaspg.com/journal/18/article/744 https://americaspg.com/storage/8537.pdf