Mobile phone has become necessary commodity in our daily life. With the advancement in Wireless Network, the number of mobile phone users has been drastically increased. Moreover mobile phone usage is not limited within voice call and SMS service, but also has become a most popular equipment of accessing Internet anytime anywhere. Different mobile apps are nowadays available for online shopping, health monitoring, playing game, watching video etc. Fifth generation mobile network aims to use multi-tier heterogeneous cellular network integrated with cloud computing to provide users latency and energy-aware service. However, for ultra-low latency and high bandwidth real time access edge/fog computing comes into the scenario. In edge/fog computing the intermediate devices between end users and cloud participate in processing and storage of data as well as execution of applications. Mobile edge computing (MEC) provides the cloud computing services at the edge of mobile network, which facilitates the developers, service providers as well as the users. In MEC the operators can open their Radio Access Network edge to the authorized third parties in order to provide rapid and flexible deployment of interactive services and applications for the users. Computations are performed usually at the local network edge in edge computing instead of putting it to the remote cloud, that in turn reduces the latency. MEC can become a principal element for the following domains:

· Video analytics

· Location based service provisioning

· Internet of Things (IoT)

· Content distribution

· Data caching

· Augmented reality

Through the deployment of various services and caching content at the network edge, mobile core networks are lighten of further congestion and can efficiently serve local purposes. MEC can be applied in various domains such as microservices, Internet of Things (IoT) etc. IoT has become a principle component to design smart technological solutions for our daily life, for example, smart home, smart city, smart healthcare, smart retail etc. IoT is usually based on Wireless Sensor Network (WSN) that is used for monitoring activities of different objects is well accepted. However, it is bound in a small area. Cloud computing has been integrated with WSN to process and store the collected sensor data inside the cloud servers. For low latency and high bandwidth services, edge computing assisted IoT has become the pillar for the development of smart home, smart health, smart traffic management, smart city etc. Mobile edge computing with IoT can improve the service quality of the existing smart solutions through the mobility analysis of users, trajectory analysis, user movement pattern based resource allocation etc. However, the IoT based mobile edge and cloud computing can face several challenges also such as latency and energy optimization, resource utilization, reliability management, security and privacy, pricing and billing, trust management etc. Artificial Intelligence can play an important role to deal with these issues through the deployment of machine learning algorithms. Nevertheless, conventional machine learning methods are applied in resourceful cloud computing platforms, whereas edge devices have limited computing and communication resources. Hence, integration of machine learning with edge computing is also a challenge. However, adapted deployment of machine learning algorithms can empower edge computing to improve the service quality.