This paper addresses the challenges in forecasting electrical energy in the current era of renewable energy integration. It reviews advanced adaptive forecasting methodologies while analyzing the evolution of research in this field through bibliometric analysis. The review highlights key contributions and limitations of current models, emphasizing the challenges of traditional methods. The analysis reveals that LSTM networks, optimization techniques, and deep learning have the potential to model the dynamic nature of energy consumption but also have higher computational demands and data requirements. The paper aims to provide a balanced view of current advancements and challenges in forecasting methods, guiding researchers, policymakers, and industry experts. It advocates for collaborative innovation in adaptive methodologies to enhance forecasting accuracy and support the development of resilient, sustainable energy systems. The paper presents a comprehensive bibliometric analysis of high-impact publications from the Scopus database from 2015 to the present. It includes over seventy seminal papers that form the backbone of this review. The analysis reveals an upward trend in scholarly publications, reflecting the field’s dynamism and growing interest in adaptive forecasting methods. The paper explores various forecasting categories, including electricity demand forecasting, deep learning, neural networks, and time series prediction. It discusses optimization algorithms, environmental and climate considerations, economic factors, and hybrid forecasting models. The review identifies sixteen significant challenges, including technical complexities in machine learning algorithms, data governance issues, and the impact of external events like the COVID-19 pandemic. These challenges highlight the need for new techniques and approaches to adapt and evolve within this ever-changing landscape. The paper also discusses the main methodologies used in adaptive forecasting, such as deep learning, renewable energy approaches, environmental and agricultural applications, economic and price forecasting, and advanced methodologies and comparisons. It highlights the transformative potential of deep learning techniques across various sectors, emphasizing their capability to address complex challenges and drive innovation. The review identifies key constraints, including data availability and quality, model limitations, computational challenges, environmental and external factors, technical challenges, real-time forecasting, hardware and technological limitations, economic and financial constraints, socio-political and regulatory challenges, and geographical and topographical constraints. These constraints underscore the need for interdisciplinary approaches and collaboration to address the myriad challenges and drive the field forward. The paper concludes that future research should expand the scope of studies beyond current limitations, focusing on broader applications and long-term forecasting.This paper addresses the challenges in forecasting electrical energy in the current era of renewable energy integration. It reviews advanced adaptive forecasting methodologies while analyzing the evolution of research in this field through bibliometric analysis. The review highlights key contributions and limitations of current models, emphasizing the challenges of traditional methods. The analysis reveals that LSTM networks, optimization techniques, and deep learning have the potential to model the dynamic nature of energy consumption but also have higher computational demands and data requirements. The paper aims to provide a balanced view of current advancements and challenges in forecasting methods, guiding researchers, policymakers, and industry experts. It advocates for collaborative innovation in adaptive methodologies to enhance forecasting accuracy and support the development of resilient, sustainable energy systems. The paper presents a comprehensive bibliometric analysis of high-impact publications from the Scopus database from 2015 to the present. It includes over seventy seminal papers that form the backbone of this review. The analysis reveals an upward trend in scholarly publications, reflecting the field’s dynamism and growing interest in adaptive forecasting methods. The paper explores various forecasting categories, including electricity demand forecasting, deep learning, neural networks, and time series prediction. It discusses optimization algorithms, environmental and climate considerations, economic factors, and hybrid forecasting models. The review identifies sixteen significant challenges, including technical complexities in machine learning algorithms, data governance issues, and the impact of external events like the COVID-19 pandemic. These challenges highlight the need for new techniques and approaches to adapt and evolve within this ever-changing landscape. The paper also discusses the main methodologies used in adaptive forecasting, such as deep learning, renewable energy approaches, environmental and agricultural applications, economic and price forecasting, and advanced methodologies and comparisons. It highlights the transformative potential of deep learning techniques across various sectors, emphasizing their capability to address complex challenges and drive innovation. The review identifies key constraints, including data availability and quality, model limitations, computational challenges, environmental and external factors, technical challenges, real-time forecasting, hardware and technological limitations, economic and financial constraints, socio-political and regulatory challenges, and geographical and topographical constraints. These constraints underscore the need for interdisciplinary approaches and collaboration to address the myriad challenges and drive the field forward. The paper concludes that future research should expand the scope of studies beyond current limitations, focusing on broader applications and long-term forecasting.