## 1 Introduction  
The foundational investigation into galaxy morphology significantly contributes to the discourse surrounding processes responsible for cosmic formation and evolution. Nevertheless, accurately categorizing galaxies by their morphological features presents substantial challenges due to diverse classifications, ambiguities in visual interpretation, and the complexity of galactic images themselves [1]. This complexity stems primarily from diverse physical circumstances and environmental interactions experienced by celestial bodies, culminating in a broad spectrum of structural characteristics and morphological indicators. The continual advancement in observational technology, including more powerful telescopes and sophisticated instruments, has exponentially increased the volume of astronomical data. Consequently, astronomers possess extensive image datasets that, although valuable for in-depth analyses, introduce significant issues regarding the management and accuracy of data interpretation as well as the leaving carbon footprint [2]. Manual classification of these extensive datasets has revealed itself as inefficient and prone to inconsistencies [3]. To overcome the described limitations inherent in manual image classification, previous propositions have suggested automating this procedure through the employment of advanced machine learning methodologies. Particularly noteworthy among these methods is the convolutional neural network (CNN), extensively employed in image-based analyses. Despite successes, pre-existing publicly accessible datasets display limitations, notably the absence of various galaxy categories and the presence of low-quality imaging data, consequently restricting CNN model generalizability. Additionally, classification and precise localization of celestial objects require balancing model granularity with generalization capacity. In response to these outlined challenges, this study is conducted through a logical progression of methodological steps. Initially, multiple models, along with CNN models, are designated for evaluation, aiming to examine their capability in accurately classifying astronomical imagery obtained from the Galaxy Zoo dataset [4]. The evaluation phase involves determining several performance diagnostics, monitoring not only the performance but also computational fluctuations over temperature. These metrics are indicators of model efficiency, effectiveness, sustainability, and applicability in embedded computing systems. This selection of diagnostics embraces the concept of Green AI (Artificial Intelligence) by pondering energy efficiency and minimal environmental impact. Simultaneously, emerging developments in computational technology have inspired collaborative projects between public organizations and leading corporations. These collaborations pursue sophisticated algorithmic solutions that address complex space-science objectives, including asteroid detection and the search for extraterrestrial life. NASA particularly employs advanced ML techniques designed to detect rare biological signatures within massive data quantities gathered from next-generation telescopes [5; 6]. From these investigative circumstances emerge essential research inquiries. Primarily, can existing pre-trained CNN models effectively analyze astronomical image datasets? Moreover, among the tested models, which demonstrate superior performance across specified performance metrics? Lastly, the present investigation proposes future enhancements to datasets, enabling subsequent evaluations of the identified top-performing models. Addressing these questions provides a reliable benchmark and facilitates insights regarding automated analysis efficacy, potentially augmenting exploration accuracy within astronomy and refining navigation capabilities in space exploration contexts. The structure of this paper is organized to ensure a systematic presentation of research methodology, analytical techniques, and experimental steps, as well as the obtained outcomes.