<p>In the contemporary era, where data-driven technologies play a pivotal role in various fields, their application in place marketing and urban development is of significant importance. This study focuses on employing these technologies to enhance the competitiveness and attractiveness of various geographical areas, including countries, regions, and municipalities, in the context of economic growth, cultural diversity, and social connections. The research utilizes data that can be requested from statistical offices and openly accessible sources, focusing on aspects like satisfaction, success in attracting capital, development, values, liveability, tourism, and strategy of individual settlements. Machine learning models, including linear regression, polynomial regression, decision tree, and random forest, are compared and evaluated using cross-validation techniques. The best ensemble-based model predicts satisfaction indices of settlements with an average error of less than 13%, even with a small training dataset. This study underscores the role of place marketing strategies in urban development, particularly where data is scarce, and highlights the importance of effective place marketing strategies in attracting investments, promoting tourism, and enhancing municipal appeal. The novel approach of predictive modelling with machine learning in place marketing, supported by proper scaling, provides a new perspective for decision-makers. This approach allows for more accurate and efficient planning, enhancing financial appeal and contributing to the sustainable development and prosperity of municipalities. The successful integration of data-driven technologies in place marketing sets a precedent for future research and practical applications in urban development.</p>