This study examines the evolution of fake news research from 2013 to 2022, identifying key trends, thematic clusters, and linkages to Sustainable Development Goals (SDGs). A bibliometric analysis of 9678 publications reveals three main thematic clusters: disinformation in social media, COVID-19-induced infodemics, and techno-scientific advancements in auto-detection. The research introduces three novel contributions: (1) mapping fake news research to SDGs, highlighting its impact on health (SDG 3), peace (SDG 16), and industry (SDG 9); (2) using prominence percentile metrics to identify critical research areas, such as misinformation and object detection in deep learning; and (3) evaluating the role of generative AI in the spread and realism of fake news, raising ethical concerns. The study also identifies emerging topics, including deepfake detection, the impact of fake news on vaccine hesitancy, and the role of AI in combating misinformation. The findings emphasize the need for interdisciplinary approaches to address the challenges posed by fake news, including ethical frameworks, computational tools for information verification, and policies to mitigate its impact on public trust and social stability. The research highlights the growing academic interest in fake news, particularly in the context of the COVID-19 pandemic, and underscores the importance of global collaboration and diverse academic contributions to address the issue effectively.This study examines the evolution of fake news research from 2013 to 2022, identifying key trends, thematic clusters, and linkages to Sustainable Development Goals (SDGs). A bibliometric analysis of 9678 publications reveals three main thematic clusters: disinformation in social media, COVID-19-induced infodemics, and techno-scientific advancements in auto-detection. The research introduces three novel contributions: (1) mapping fake news research to SDGs, highlighting its impact on health (SDG 3), peace (SDG 16), and industry (SDG 9); (2) using prominence percentile metrics to identify critical research areas, such as misinformation and object detection in deep learning; and (3) evaluating the role of generative AI in the spread and realism of fake news, raising ethical concerns. The study also identifies emerging topics, including deepfake detection, the impact of fake news on vaccine hesitancy, and the role of AI in combating misinformation. The findings emphasize the need for interdisciplinary approaches to address the challenges posed by fake news, including ethical frameworks, computational tools for information verification, and policies to mitigate its impact on public trust and social stability. The research highlights the growing academic interest in fake news, particularly in the context of the COVID-19 pandemic, and underscores the importance of global collaboration and diverse academic contributions to address the issue effectively.