Smart cities (SC) promote economic development, improve the welfare of their citizens, and help in the ability of people to use technologies to build sustainable services. However, computational methods are necessary to assist in the process of creating smart cities because they are fundamental to the decision-making process, assist in policy making, and offer improved services to citizens. As such, the aim of this research is to present a systematic review regarding data mining (DM) and machine learning (ML) approaches adopted in the promotion of smart cities. The Methodi Ordinatio was used to find relevant articles and the VOSviewer software was performed for a network analysis. Thirty-nine significant articles were identified for analysis from the Web of Science and Scopus databases, in which we analyzed the DM and ML techniques used, as well as the areas that are most engaged in promoting smart cities. Predictive analytics was the most common technique and the studies focused primarily on the areas of smart mobility and smart environment. This study seeks to encourage approaches that can be used by governmental agencies and companies to develop smart cities, being essential to assist in the Sustainable Development Goals.

Sustainability has been regarded as a central aspect for the short-, mid-, and long-term survival of businesses. This paradigm brings forth the need for companies to keep up with market competition while addressing the sustainable development aspects of their operations. With the emergence of tools and techniques such as machine learning and artificial intelligence language models, the decline of market entry barriers has prompted organizations to innovate and stay competitive through traditional innovation or employing open innovation (OI). The purpose of this paper is to investigate how OI can enhance sustainability practices. To this end, a systematic review using Methodi Ordinatio was conducted. We investigated authors’ objectives and overarching topics revolving around sustainability and OI. Three areas of opportunity were identified: (i) the literature on sustainability and OI is prolific, and international collaboration is encouraged; (ii) aspects of sustainability and OI are viewed through the prism of environmental concerns, sustainable innovation, social dimensions, principles of circular economy, sustainable business models, and pathways to sustainable growth; and (iii) collaboration, crowdsourcing, knowledge management, and stakeholder engagement are some of the most frequently addressed issues. Future research can be conducted on these avenues to produce significant results for the area of sustainable OI.