Irish Interdisciplinary Journal of Science & Research (IIJSR) Volume 7, Issue 1, Pages 83-89, January-March 2023 ISSN: 2582-3981 https://iijsr.com 83 An Entity of the Ocean Data Prediction System Using Machine Learning Techniques S.Oviya 1* , Dr.T.Ananth Kumar 2 , Dr.D.Jessintha 3 , Dr.R.Rajmohan 4 , N.Padmapriya 5 & Dr.S.Arunmozhi selvi 6 1,2,5 Department of Computer Science and Engineering, IFET College of Engineering, Tamilnadu, India. 3 Department of Electronics and Communication Engineering, Easwari Engineering College, Tamilnadu, India. 4 School of Computer Science and Engineering, VIT-AP University, Amaravati, India. 6 Principal, Holy Cross Engineering College, Tamilnadu, India. Corresponding Author (S.Oviya) Email: oviyakoliyanur@gmail.com* DOI: https://doi.org/10.46759/IIJSR.2023.7112 Copyright © 2023 S.Oviya et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Article Received: 19 February 2023 Article Accepted: 27 March 2023 Article Published: 30 March 2023 ░ 1. Introduction With advances in technology and data processing capabilities, the amount of marine data available is increasing rapidly. This presents new opportunities for research and innovation and new challenges for data management and analysis. Effective use of marine data requires collaboration between scientists, policymakers, and stakeholders from different sectors to ensure that the information is used to benefit society and protect the ocean environment [1]. Ocean data is data collected from the oceans and other marine environments. It includes observations from the sea surface, subsurface, and bottom and data from satellites, buoys, tags, and other instruments placed in the oceans. Examples of ocean data include temperature and salinity readings, current speeds and directions, wave heights and spectra, chemical concentrations in ocean water, ocean floor topography and maps, and satellite imagery [2]. Ocean data is used in various fields, from climate research and marine biology to search and rescue operations and fishing management. It needs the proper data status accuracy and loss prediction for the best climate weather prediction as it performs according to the feasibility of the input data. The significant development of machine learning (ML) over the last few decades has expanded the use of this data-driven methodology in research, business, and industry. There has recently been an increase in interest in using ML to address data quality and precision issues. The Bi-LSTM Neural network in particular, has been utilized with ML methods to create a functional system [3]. Accurate and the loss value gained from this system will enhance the weather prediction process. Recurrent neural network (RNN) architectures such as the Bidirectional Long Short-Term Memory (Bi-LSTM) algorithm process ABSTRACT The tropical cyclone is one of the most powerful and destructive meteorological systems on Earth. Researchers note tropical cyclone data every few seconds, but utilizing all of the data with the appropriate accuracy values is difficult. In this system, we predict the various elements' status accuracy and loss in the ocean data set. The use of machine learning methods has developed a lot, and the prediction of the value of the ocean data follows the new enhanced term to give the status of the elements in the data. The LSTM (long short-term memory neural network excavation model) of the historical track's helpful information is more profound and more precise. Bi-LSTM goes the both forward and backward directions, and Adam optimizer, two updated machine learning techniques, are utilized to assess the status of the ocean element in the data set. It goes beyond the existing system to offer an opportunity for a different system result. The data set with a large number of values will also perform accurately. The project's ultimate objective is to give oceanographers a tool to anticipate the quality of ocean data in real-time, which can increase the precision of climate models and help with improved ocean-related decision-making. Keywords: Machine Learning; Ocean DATA; Bi-LSTM; Adam Optimizer.