# Machine Learning for Blockchain and IoT Systems in Smart Cities: A Survey
## 4 Synergy of Machine Learning, Blockchain, and IoT in Smart Cities
### Distinct Interactions and Complementary Roles  
While ML, blockchain, and IoT share several similarities in their approach to enhancing smart city infrastructure, each technology also brings distinct capabilities and plays complementary roles, essential for developing intelligent urban environments. The cooperation between ML, blockchain, and the IoT in smart cities goes beyond mere technological integration; it embodies a sophisticated interplay of advanced computational models, decentralized frameworks, and interconnected systems that collectively enable the evolution of urban environments into intelligent, adaptive, and secure ecosystems. The confluence of these technologies empowers smart cities to operate at an unprecedented level of efficiency and resilience by facilitating real-time decision-making, enhancing data security, and enabling seamless collaboration across diverse urban subsystems [79; 80; 81; 82]. At the core of this synergy lies the ability of ML algorithms to extract actionable insights from the vast, heterogeneous data generated by IoT devices. These insights drive autonomous decision-making processes that are crucial for managing the dynamic and complex nature of urban systems. For example, ML models can continuously learn from traffic sensor data, optimizing traffic flows and reducing congestion in real time. In energy grids, ML predicts consumption patterns, optimizing energy distribution and reducing waste, which are vital for sustainability in densely populated urban areas [83; 84; 85; 86]. Blockchain technology, with its decentralized and immutable ledger, complements ML by ensuring that the data feeding these models are both secure and trustworthy. The integrity of data is paramount in smart cities, where decisions impact millions of lives. Blockchain's consensus mechanisms and cryptographic protocols ensure that data originating from IoT devices remain unaltered and verifiable, which is critical when ML models are used to automate crucial services like emergency response, healthcare, or financial transactions [87; 88; 89; 90]. Moreover, the integration of smart contracts within blockchain frameworks allows for the automation of processes based on predefined conditions, further enhancing the efficiency of IoT systems. For instance, in smart grids, blockchain-enabled smart contracts can automatically execute transactions between energy producers and consumers, based on real-time data processed by ML algorithms. This not only streamlines operations but also minimizes human intervention, reducing the potential for errors and fraud [91; 92; 93; 94]. The interoperability between these technologies facilitates a level of collaboration across different sectors of a smart city that would be impossible with siloed systems. For example, ML can be employed to forecast maintenance needs for infrastructure, while blockchain securely records all relevant data and transactions, ensuring transparency and accountability. This creates a feedback loop where data are continuously refined and used to improve both the efficiency and security of urban systems [95; 96; 97; 98]. Additionally, the combination of ML, blockchain, and IoT fosters an environment where data privacy is rigorously protected while enabling the data-driven innovation that smart cities require. FL, an advanced ML technique that allows models to be trained on decentralized data, can be coupled with blockchain to ensure that sensitive data never leave their source while contributing to global models. This is particularly crucial in areas such as healthcare, where privacy concerns are paramount [99; 100; 101; 102]. In a nutshell, the synergy of ML, blockchain, and IoT in smart cities is not merely a combination of technologies but a sophisticated ecosystem where data-driven intelligence,security, and autonomy are seamlessly integrated to create urban environments that are not only smart but also resilient, sustainable, and responsive to the needs of their inhabitants.