## 2 Related Studies--A Brief Review  
Artificial intelligence (AI) has caused significant reorganisation in many different industrial and scientific sectors as machines learn how to solve specific problems [13]. Computer algorithms acquire knowledge of the fundamental connections within a provided dataset and autonomously detect patterns to make decisions or forecasts [14]. Machinelearning (ML) algorithms enable machines to implement intellectual activities by applying complex mathematical and statistical models [15]. Specifically, supervised and unsupervised ML methods have played an essential and effective role in optical communication, especially in detecting impairments and performance monitoring in UWOC systems. ML methods are important interdisciplinary tools that utilise eye diagram images as feature sources in several fields, including computer vision [16], equalisation [17], signal detection, and modulation format identification [18]. Some examples of techniques used in this study include Support Vector Machine (SVM) [19], k-means clustering to mitigate nonlinearity effects [20; 21], Principal Component Analysis (PCA) for modulation format identification (MFI), optical signal-to-noise ratio (OSNR) monitoring [22], and Kalman filtering for OPM [23]. Reference [24] indicated that CNNs could achieve the highest accuracy compared to five other ML algorithms: Decision Tree (DT), K-Nearest Neighbour (KNN), Back Propagation (BP), Artificial Neural Networks (ANNs), and SVM. Figure 1 depicts the various applications of ML algorithms used in optical communication. This study provides a comprehensive review of ML solution applications in UWOC technology. Neural networks, such as ANNs, CNNs, and recurrent neural networks (RNNs) are highly suitable machine learning tools. They are capable of learning the complex relationships between samples or extracted features from symbols and channel parameters such as optical signal-to-noise ratio (OSNR), polarisation mode dispersion (PMD), polarisation-dependent loss (PDL), baud rate, and chromatic dispersion (CD) [10; 25; 26; 27; 28; 29; 30; 31; 32; 33]. The OSNR is a signal parameter that significantly impacts the effectiveness of optical links. The OSNR can be used to predict the bit error rate (BER), which directly gauges receiver performance [34]. Reference [35] proposed and demonstrated a system for compensating for fibre nonlinearity impairment using a simple recurrent neural network (SRNN) with low complexity. This method reduces computational complexity and training costs while maintaining good compensation performance. Several methods based on automatic feature extraction can be used to obtain the features input into the neural network (NN). These methods use constellation diagrams, asynchronously amplitude histograms (AAHs), Asynchronous Delay Tap Plots (ADTPs), Asynchronous Single Channel Sampling (ASCS), In-phase Quadrature Histograms (IQHs) for SNR, and other parameter estimations. Here, we review the various works that display a range of OPM works utilising machine learning techniques to forecast signal-to-noise ratio (SNR) values through various approaches. In [36], a new machine learning OPM method is proposed that uses support vector regressors (SVRs) and modified In-phase Quadrature Histogram (IQH) features to estimate several optical parameters, including signal-to-noise ratio (SNR) and chromatic dispersion (CD). A deep learning algorithm in ref. [37] has been successfully applied in wireless communications, but it often results in challenging nonlinear problems. An ANN algorithm in [26] was developed to calculate the signal-to-noise ratio (SNR) using On-Off Keying (OOK) and Differential Phase Shift Keying (POM) [27; 28; 29; 30; 31; 32; 33]. Figure 1: ML algorithms in optical performance monitoring. (DPSK) data. The training errors for OOK and DPSK were 0.03 and 0.04, respectively. The ANN is trained by sending a series of well-known symbols before being used as an equaliser.