## 1 Introduction  
Today, machine learning (ML) is frequently used in intelligent systems to provide these systems with the ability to learn from problem-specific training data and to automate the construction of analytical models and the process of solving associated tasks [1]. ML techniques are widely utilised in advertising, recommendation systems, computer vision, natural language processing and user behaviour analytics. Given the wide range of applications and requirements, various ML methods have been developed to provide solutions for user needs and overcome various issues and challenges efficiently. Incorporating artificial intelligence into network infrastructure is one of the industry strategies for addressing the inherent complexities associated with the 5G network deployment. The expanding usage of wireless technology is concerned to possibly congest the airwaves that our devices utilise to communicate; thus, ML replaces conventional wireless technologies to decrease power consumption substantially and improve network performance [2]. In general, developing an efficient ML model is a complicated and time-consuming process that entails selecting the best algorithm and achieving the best model architecture by optimising its hyperparameters [3]. Hyperparameters are the variables that determine the network structure and how the network is trained. Each ML model has a unique set of hyperparameters, which require different settings when trained on different datasets. Hyperparameter tuning is a process of finding the best settings for the ML algorithm. The goal is to find the parameters that produce high accuracy and low error rate. In ML, many algorithms with different featuresexist. These algorithms include logistic regression, linear regression, support vector machines (SVMs) and random forests (RFs). Each algorithm has its own optimisation method that can be used to optimise the model parameters. For example, in logistic regression a parameter is called lambda value can be optimised using multiple methods such as the gradient descent method, modified Newton-Raphson method or stochastic gradient descent method. Other intelligent algorithms, such as stacked denoising autoencoders, convolutional networks and classifiers based on sophisticated feature extraction techniques, have between 10 and 50 hyperparameters. The settings of hyperparameters depends on how the experimenter chooses to parametrise the model and how many hyperparameters are fixed at a reasonable default [4]. Published results are difficult to repeat and expand because of the difficulties in calibrating these models, resulting in challenges in optimising a loss function across a graph-structured configuration space, known as hyperparameter optimisation. A hyperparameter optimisation method must optimise across discrete and continuous variables whilst choosing which variables to optimise [4]. Therefore, frequently optimising the prediction model necessitates a thorough understanding of both ML methods and appropriate hyperparameter optimisation approaches. Although numerous automated optimisation strategies exist, their benefits and downsides vary when applied to different challenges, particularly those concerned with 5G technology. Thus, we anticipate that learning the model's behaviour and characteristics allows the excellent optimisation of processing power, time and accuracy [3]. In 5G, a base station must process numerology assignment and resource allocation for each user according to the user's feedback. ML offers an advantage in providing rapid adaptation to assign the allocated time, space and frequency domain in the spectrum and satisfy the user's Quality of Service (QoS) requirement [5]. A base station also needs to decide on the optional waveform processing techniques, including windowing, filtering, guard utilisation, cyclic prefix utilisation and different operating subcarrier spacings.