Financial markets are beneficial to individuals in various domains such as financial, corporate, businesses and banking. Today, artificial intelligence performs a pivotal role in financial markets by using its vast set of capabilities and computing resources. This technology is widely used in financial forecasting, valuation, business analysis, resource planning, investment strategy and other business fields. Traders and investors are using machine learning models to predict trends in financial instruments. Since artificial intelligence is being extensively used today in finance, and becomes imperative to encapsulate the contemporary understandings of machine learning and deep learning. This makes it easier to compare and thoroughly examine different machine learning models and methodologies in the financial arena. This article investigates various techniques and algorithms such as Long short-term memory (LSTM) and Autoregressive Moving Averages (ARIMA). Prophet (developed by Meta) and NLP based sentiment analysis model to forecast the movement of stock exchange. The key findings and takeaways from this research paper are as follows: (a) provides an overview of financial models in machine learning and deep learning; (b) provides a general framework for cost estimation and allocation; (c) using and testing the performance of various trading strategies and combination models to predict market value and compare the results to analyze which strategy-based model delivers best performance.