Cities are not static; they are composed of complex couplings of several components, more particularly between the current and past urban form, and act as a kind of palimpsest. Their evolution and interaction involve leveraging, layering and retaining history while incorporating the emerge of new urban forms. Transformations and spatial patterns of physical forms are the basis of urban morphological studies. These studies aim to understand and interpret process-based transitions, transformations, and spatial patterns of physical components. The concept of palimpsest explains continuous construction over time (historical stratification), but the scale of urban palimpsest is more than that. The field of urban morphology has been used the concept of the palimpsest through a qualitative approach (traditional urban morphology), with diachronic and evolutionary aspects. However, there is a challenge in conceptually integrating quantitative insights that could acknowledge numerical and longitudinal characteristic. The examination of urban form at various scales have been analysed in both qualitative and quantitative ways. Yet, the lack of a systematic and comprehensive investigation of morphology often anticipates a systematic distinction between different levels of complexity during processes of formation and change. Since, morphological evolution occurs at different levels of scale, the result is that the variance of persistency of change is a spatial aggregation of processes. Cross-examinations with qualitative and quantitative methods can help obtain information about different types of form in different categories. This information can then be compared with other possible information. This thesis is therefore devoted to establishing a more robust, and comprehensive method. It uses existing qualitative knowledge of urban morphology and integrates it with new approaches to urban morphology, science and technology. The aim is to address the link between the qualitative features and quantification of urban from through temporal and spatiotemporal layering. As the complexity of cities increases over time, the large amount of data obtained makes pattern identification challenging. This difficulty is addressed in this thesis by linking urban morphology with machine learning and artificial intelligence. The content of the thesis first focuses on explaining the concept of palimpsest by introducing the origin of the “palimpsest” term. It then reviews the term of urban morphology, different approaches to urban morphology, and different disciplines that have the potential to be methodologically linked with palimpsest concept. Based on this literature review, the gap in knowledge is identified. After that, the thesis introduces three Ottoman capital cities in Türkiye: Bursa, Edirne and Istanbul. These three case studies have previously been investigated morphologically, but without integrating a longitudinal and diachronic frame. The proposed method is applied to two former Ottoman capital cities in Türkiye, Bursa and Edirne. These cities demonstrate historical heterogeneity and morphological variations of Ottoman urban structures. Therefore, they are used to validate inherited patterns through qualitative and quantitative analysis. The last Ottoman capital city, Istanbul, demonstrates the historical and cultural heterogeneity of different empires. It is integrated as a case study site to test the morphological validity of Ottoman patterns. Furthermore, the robustness of the proposed method is tested through the assessment of different case study sites from different continents, including the USA and Europe. The research is accompanied by GIS and Python for quantitative investigation of urban patterns. This provides a computational frame to empirically confirm whether patterns of changes are truly positioned at different levels of scale and periods. Lastly, the results produced by human and urban morphological analysis are incorporated with artificial intelligent technologies for better reasoning in the process of pattern identification. The robustness of this method may shed new light on future urban studies by integrating different approaches, methods, and technologies to understand the formation and transformation process of cities.