With the evolution of complexity in today’s markets and investment opportunities, traditional methods of appropriate multi-asset portfolio optimization have become insufficient, exposing the funds to overly large levels of risk. This paper addresses the question of how decision-making and investment strategies involving portfolios are assisted by artificial intelligence (AI) and, in particular, machine learning (ML). Everything, including processes such as supervised learning, unsupervised learning, and reinforcement learning, is studied in the context of critical tasks within portfolio management such as investment, risk, and trading strategies. A portfolio processed using AI reported an increase of 12% in predictive accuracy and a decrease of 20% in computation time for backtesting simulations. This was mainly due to well-developed noise filtering features that enable the model to operate in unstable market conditions. Even the key results show that in terms of risk-adjusted performance and managing uncertainty in the market, AI-based models are far better than traditional approaches. In addition, this research draws attention to emerging data sources and ethical AI, AI-based models and practices that further increase transparency, and AI methods associated with ethical concerns and data biases. These results demonstrate how something like AI stands to completely revolutionize the concept of investment and the strategies that accompany it, bringing about a form of change that is very much needed in today’s financial system.