This research presents a comprehensive bibliometric review of the role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing production efficiency and fostering sustainable development. With the increasing focus on sustainability, AI and ML technologies have emerged as pivotal tools for optimizing industrial processes, improving resource management and minimizing environmental impacts. The study analyzes key ML algorithms in various production settings. This study conducts systematic bibliometric analysis using the Scopus database and Bibliometrix R package, examining global trends, key collaborations, and thematic focuses on AI and ML applications in production efficiency and sustainable development. Novel contributions include uncovering underexplored ethical dimensions of AI adoption and emphasizing the pivotal role of SMEs and developing economies in advancing sustainable practices. Key research trends identified include the integration of AI with sustainable energy management, circular economy practices, and precision agriculture. Furthermore, the analysis reveals geographical contributions, with countries like China, the United States, and the United Kingdom leading in research output and impact. Despite the promising advancements, the review identifies gaps in ethical considerations, especially in data privacy and labor market implications, and suggests avenues for future research, including the implementation of AI and ML in developing economies and Small and Medium Enterprises (SMEs).

This chapter undertakes a systematic bibliometric analysis of hierarchical clustering applications within the social sciences and humanities, drawing upon publications indexed in the Scopus database from 1975 to 2024. The findings reveal the methodological versatility of clustering techniques in addressing complex research problems across a broad spectrum of academic disciplines. Thematic emphases on social network analysis and data management strategies underscore the centrality of clustering in elucidating intricate sociocultural systems. The United States, China, and the United Kingdom emerge as principal contributors, highlighting the global scope and interdisciplinary nature of this research domain. This analysis provides a comprehensive account of prevailing thematic trends, patterns of scholarly collaboration, and the intellectual architectures that underpin the field. It offers a robust foundation for both the methodological advancement of clustering techniques and their expanded application to pressing societal and academic challenges

This study provides a comprehensive systematic review and bibliometric mapping of artificial intelligence and machine learning applications within sustainability research. A bibliometric analysis was conducted on 2981 publications retrieved from the Scopus database, covering the period from 2003 to 2025 and tracing the field’s evolution from fragmented early studies to rapid growth after 2018. The findings reveal a robust methodological core centered on deep learning and neural networks, increasingly applied to energy efficiency, precision agriculture, and smart urban ecosystems. A critical contribution of this review is the identification of the emergence of Green AI, highlighting the dual challenge of using artificial intelligence for environmental goals while mitigating the carbon footprint of computational processes themselves. Ultimately, this study offers a strategic roadmap for researchers and policymakers to align algorithmic innovation with global sustainable development goals.

This research explores the growing role of machine learning in social sciences and humanities research, aiming to provide a comprehensive bibliometric review of its applications, trends, and impact. A systematic approach was employed, combining bibliometric analysis and visualization techniques to analyze a dataset extracted from the Scopus database, focusing on scholarly works related to machine learning in disciplines such as sociology, philosophy, history, and economics. The analysis highlights the increasing volume of publications and the evolving research landscape, with notable growth in recent years, particularly after 2005. Key findings indicate that machine learning is primarily applied in areas such as decision-making, sustainability, social media analysis, and natural language processing, reflecting its diverse potential in addressing complex societal issues. The study also reveals a strong collaborative nature, with a significant percentage of publications involving international co-authorship. Geographic analysis shows that countries like the United Kingdom, India, and the United States are leading contributors, while emerging nations also make noteworthy contributions. The findings suggest that machine learning is becoming an essential tool for social and cultural research, offering new insights into behavioral patterns, governance, and global challenges. In conclusion, the interdisciplinary nature of this approach is emphasized, along with the need for continued methodological innovation and cross-border collaboration to enhance its impact in these fields.

This study presents a systematic bibliometric evaluation of artificial intelligence methodologies applied to the preservation and interpretation of Asia Minor’s cultural heritage. Publication trends demonstrate notable continuity, with foundational works sustaining their citation impact over a span of twenty-five years, thereby underscoring enduring scholarly engagement. Network analyses of keyword co-occurrence delineate a conceptual core organized around immersive visualization, exemplified by terms such as cultural heritages, virtual reality, and photogrammetry, while temporal mappings reveal the recent integration of machine learning and deep learning paradigms. Collectively, these findings chart an intellectual landscape in which three-dimensional reconstruction constitutes the foundational axis of research, now progressively enriched by data-driven algorithmic approaches. This synthesis offers a concise yet comprehensive portrait of evolving methodological trajectories and emerging computational frontiers in AI-driven heritage scholarship.

This study investigates tax evasion and the informal economy in Greece through an integrated research design that combines bibliometric analysis with large-scale survey data to examine both the structure of scholarly discourse and public perceptions of economic non-compliance. The analysis integrates a bibliometric analysis of the academic literature with survey data from 1074 respondents, enabling patterns of scholarly attention to be assessed alongside public evaluations of institutional performance and economic behavior. The bibliometric findings indicate that academic research is organized around Greece and the tax system as central reference points, while governance-related themes such as transparency and public policy occupy comparatively peripheral positions within the thematic landscape, suggesting a field structured predominantly around country-specific institutional analysis. The survey results reveal a broadly comparable configuration, with political institutions, corruption, and tax evasion identified among the most salient national problems. Respondents differentiate among distinct forms of economic non-compliance and attribute tax evasion primarily to systemic factors, including high taxation, perceived injustice, ineffective revenue management, and corruption, rather than to individual moral failings. Overall, tax evasion in Greece is thus evaluated predominantly in institutional and governance-related terms. Future research could extend this approach through longitudinal bibliometric mapping, multivariate survey modelling, and sectoral or regional comparative analyses.