## 1 Introduction  
Unreinforced masonry (URM) buildings constitute a significant portion of residential and heritage structures worldwide. Notably, such buildings represent a considerable number of the occupied buildings in developing nations such as India, Nepal, Pakistan, and Thailand [1, 2]. These structures undergo extensive damage or complete collapse during seismic events due to their poor lateral load-bearing capacity. Hence, seismic response prediction and damage classification in URM buildings is critical due to the existence of non-engineered URM buildings in several earthquake-prone regions of the world [3]. Furthermore, modern computational methods involving machine learning algorithms will suitably aid in the effective seismic risk assessment of URM buildings. The investigation of the seismic response of URM buildings holds significant importance for enhancing the structural safety of the buildings. It is also essential to formulate effective strategies to safeguard these vulnerable structures during seismic events. Modern computational tools, specifically machine learning algorithms, present a promising alternative to time-consuming numerical and analytical simulations, addressing the critical need for seismic risk assessment in URM buildings more effectively. The significance of the research lies in its innovative approach to the identification of damage in URM buildings, paving the way for the development of machine learning models for seismic damage assessment in such structures. The application of machine learning in structural engineering has increased considerably in recent years. Machine learning (ML) algorithms utilize statistics to make inferences from the data to train developed models by using past experiences and behaviour patterns [4]. Hence, developing the data usually presented as predictors and response variables is crucial for implementing ML models for a particular application [5]. A comprehensive dataset will enable the ML algorithms to predict or to classify possible future samples. Since the numerical and analytical simulations of complex structural systems are time-consuming, ML could be a suitable alternative for various applications in structural engineering and earthquake engineering [6; 7]. Several studies have been conducted on the structural response prediction and damage classification associated with a wide range of structures, using ML-based models [8; 9; 10; 11; 12]. Various ML models were developed to assess the seismic response, failure mode classification, and strength estimation of reinforced concrete structures at the structural element and building levels (including the reinforced concrete shear walls, frames with infills, and beam-column joints) [9; 10; 11; 12]. In addition, ML applications in the optimization and fragility analysis of masonry-infilled reinforced concrete and steel moment frames have also been reported recently [13; 14; 15]. Studies also reported the application of ML algorithms in post-earthquake structural safety assessment, risk assessment, and fatigue analysis [16; 17; 18]. The literature clearly indicates the effectiveness of the machine learning approach in its application to complex structural-engineering problems. In the case of masonry structures, recent notable research on damage assessment using ML approaches can be found in the literature. [PERSON] et al. [19] implemented ML algorithms to assess the damage intensity of existing masonry buildings subjected to the adverse impacts of the industrial environment of mines, and better performance was obtained from the Bayesian belief network. [PERSON] and [PERSON] [20] utilized deep learning networks to improve crack detection in masonry walls and automation in brick segmentation, and the results indicated a better outcome from the ML models, compared to the typical image-processing techniques. [PERSON] et al. [21] analyzed the applicability of three ML algorithms for predicting the compressive strength of masonry constructions, establishing a database using 44 masonry specimens.