###### Abstract
This paper utilizes the acousto-optic sensor data from a real-time setup to implement the proposed method. Like the acoustic emission sensor, the acousto-optic sensor captures the acoustic vibrations in the pipeline. Widely used machine learning (ML) and DL algorithms to detect a pipeline leak are the support vector machine (SVM) [15; 29; 30; 31], naive Bayes (NB) [31], logistic regression (LR), decision tree (DT) [24; 30; 31; 33], multi-layer perceptron (MLP), k-nearest neighbors (KNN) [24; 34], random forest (RF) [18; 24], gradient boosting [24], LightGBM [24], XGBoost [24], CatBoost [24], long short-term memory (LSTM) [35], and convolutional neural network (CNN) [8; 9; 10; 14; 16; 17; 20; 21; 22; 23; 36; 37; 38; 39; 40; 41; 42]. Data collected from acoustic emission sensors, acousto-optic sensors, microphones, and vibration sensors must be feature-extracted for ML classifiers like SVM, NB, and DT. [PERSON] et al. implemented a leak detection mechanism for acoustic signal data after using wavelet denoising, feature extraction, and SVM [15]. The feature extraction techniques used were mean, standard deviation, crest factor, short-time entropy, kurtosis, skewness, frequency centroid, frequency band, peak frequency, wavelet mean frequency, and wavelet entropy. Using SVM for the extracted features, the achieved accuracy was 99.4%. [PERSON] et al. implemented a leak detection mechanism with an in-pipe optical fiber-based pressure sensor and impulse response function, achieving great accuracy [28]. [PERSON] et al. implemented a leak zone identification system using a multiclass SVM with pressure sensor data [29]. The highest accuracy obtained by the multiclass SVM was 98.07%. [PERSON] et al. compared LR, DT, NB, SVM, and MLP to identify the best classifier to detect a leak using flow meters and obtained an accuracy of 95% with LR [31]. Further, [PERSON] et al. implemented a hybrid approach using SVM and MLP to improve flow meter-based leak detection accuracy [30]. The hybrid SVM-MLP combination gave a leak detection accuracy of 99%. [PERSON] et al. implemented KNN for pipeline leak detection with time-domain features and achieved a 94.34% correct classification rate [34]. [PERSON] et al. designed a burst detection method using LSTM with flow meter data in district metering areas [35]. The LSTM detected the burst with 99.80% accuracy. [PERSON] et al. explored machine learning models like KNN, DT, RF, extra trees, gradient boosting, LightGBM, XGBoost, and CatBoost for leak detection in water pipelines using vibration sensors [24]. Time and frequency domain features were extracted and passed to these classifiers to detect leaks. The XGBoost gave the best leak detection accuracy of 99.79% among the classifiers. [PERSON] et al. implemented a leak size identification system using an acoustic emission intensity index using RF [18]. The data were collected using an acoustic emission sensor. The b-value was extracted from the acoustic emission sensor data for the acoustic emission intensity index curve construction. After the leak was confirmed, the data were passed to the RF for leak size identification. The RF gave an accuracy of 99.2% for 18-bar pressure data. [PERSON] et al. implemented a leak detection mechanism using acoustic emission sensors with machine learning algorithms [19]. Twenty-five time and frequency domain features were extracted from the acoustic emission sensor data before passing them to the ML classifiers for leak detection. With the system, the overall classification accuracy achieved was 99%. Types of CNN applied in pipeline leak detection are one-dimensional and two-dimensional. The data collected from acousto-optic sensors, acoustic emission sensors, microphones, and vibration sensors are one-dimensional and applied directly to one-dimensional CNNs. Further, the one-dimensional data are converted into two-dimensional data and applied with 2 DCNN for leak detection.