This paper presents a systematic literature review on hybrid methods combining optimization and machine learning techniques for clustering and classification. It aims to identify the potential of these methods to overcome the limitations of individual approaches. The review includes a numerical overview of works published since 1970 and an in-depth analysis of the last three years, covering 1007 papers. A SWOT analysis of the ten most cited algorithms is conducted to evaluate their strengths, weaknesses, opportunities, and threats. The paper highlights the effectiveness of hybrid methods in improving performance by leveraging the strengths of both optimization and machine learning. It discusses the importance of hybrid methods in solving complex problems and provides insights into future research directions. The review also outlines the methodology used, including keyword selection, source selection, and inclusion/exclusion criteria. The results show the growing interest in hybrid methods, with a significant increase in publications since the 2010s. The paper concludes that hybrid methods offer a powerful framework for tackling complex problems by integrating the strengths of both optimization and machine learning.This paper presents a systematic literature review on hybrid methods combining optimization and machine learning techniques for clustering and classification. It aims to identify the potential of these methods to overcome the limitations of individual approaches. The review includes a numerical overview of works published since 1970 and an in-depth analysis of the last three years, covering 1007 papers. A SWOT analysis of the ten most cited algorithms is conducted to evaluate their strengths, weaknesses, opportunities, and threats. The paper highlights the effectiveness of hybrid methods in improving performance by leveraging the strengths of both optimization and machine learning. It discusses the importance of hybrid methods in solving complex problems and provides insights into future research directions. The review also outlines the methodology used, including keyword selection, source selection, and inclusion/exclusion criteria. The results show the growing interest in hybrid methods, with a significant increase in publications since the 2010s. The paper concludes that hybrid methods offer a powerful framework for tackling complex problems by integrating the strengths of both optimization and machine learning.