## 1 Introduction  
The transportation industry has witnessed a remarkable transformation with the advent of electric and flying vehicles (EnFVs), holding the promise of enhanced sustainability, reduced congestion, and improved mobility [1; 2; 3]. These innovative modes of transportation, exemplified by electric vehicles (EVs) and unmanned aerial vehicles (UAVs), have the potential to revolutionize urban mobility and logistics. However, the effective communication and message routing within these dynamic transportation systems are faced with unique challenges that require specialized solutions [4]. Multi-objective message routing stands as a critical issue of EnFV operations, requiring the simultaneous optimization of different objectives, including travel time minimization, energy consumption reduction, reliability maximization, and environmental sustainability promotion [5; 6]. The optimization task becomes even more tedious due to the inherent conflicts among these objectives. Genetic algorithms (GAs) offer a promising avenue to address the multi-objective message routing in EnFVs [7]. Efficient communications in EnFVs are essential, given their substantial influence on the sustainability of futuristic transportation [8]. Embracing electric and energy-efficient solutions not only reduces operational costs but also significantly mitigates the adverse effects of global warming.  
In light of these challenges and opportunities, this review paper sets out to provide a comprehensive analysis of the utilization of GAs for multi-objective message routing in EnFVs. By conducting an extensive examination of existing research papers and studies, this paper seeks to outline state-of-the-art approaches and methodologies, identify key challenges, discuss the merits and limitations of GA-based routing strategies, and infer their implications for realizing the efficiency, reliability, and sustainability of message routing in EnFVs. Our novel work targets to answer pivotal questions given below, that roam in the research community regarding the applications of the GA in EnFVs environment. Why is a thorough examination and analysis of the application of GA for multi-objective message routing in EnFVs missing from the literature? What approaches have been employed so far in multi-objective message routing for EnFVs? What are the key challenges and complexities associated with multi-objective message routing within dynamic EnFV systems? What are the benefits and limits of GA-based routing strategies in the context of EnFVs? What are the practical implications of employing GA-based routing on the efficiency, reliability, and sustainability of message routing in EnFVs?  
A comprehensive and thorough answer to the questions given above collectively contributes to a comprehensive understanding of the role of genetic algorithms in addressing the multi-objective message routing challenges in electric and flying vehicles, offering insights into their potential benefits and limitations. Moreover, this review paper identifies future research directions and opportunities to enhance the effectiveness of GAs in optimizing message routing for EnFVs. By providing critical insights into the current state of research and potential areas for improvement, this study aims to contribute to the advancement of efficient and reliable communication within these innovative transportation systems.