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The rise or fall of the stock markets directly affects investors' interest and loyalty. Therefore, it is necessary to measure the performance of stocks in the market in advance to prevent our assets from suffering significant losses. In our proposed study, six supervised machine learning (ML) strategies and deep learning (DL) models with long short-term memory (LSTM) of data science was deployed for thorough analysis and measurement of the performance of the technology stocks. Under discussion are Apple Inc. (AAPL), Microsoft Corporation (MSFT), Broadcom Inc., Taiwan Semiconductor Manufacturing Company Limited (TSM), NVIDIA Corporation (NVDA), and Avigilon Corporation (AVGO). The datasets were taken from the Yahoo Finance API from 06-05-2005 to 06-05-2022 (seventeen years) with 4280 samples. As already noted, multiple studies have been performed to resolve this problem using linear regression, support vectormachines, deep long short-termmemory (LSTM), and many other models. In this research, the Hidden Markov Model (HMM) outperformed other employed machine learning ensembles, tree-basedmodels, the ARIMA (Auto Regressive IntegratedMoving Average)model, and long short-term memory with a robust mean accuracy score of 99.98. Other statistical analyses and measurements for machine learning ensemble algorithms, the Long Short-TermModel, and ARIMA were also carried out for further investigation of the performance of advanced models for forecasting time series data. Thus, the proposed research found the best model to be HMM, and LSTM was the second-best model that performed well in all aspects. A developedmodel will be highly recommended and helpful for earlymeasurement of technology stock performance for investment or withdrawal based on the future stock rise or fall for creating smart environments. [ABSTRACT FROM AUTHOR]