In modern markets, more than 70% of all trades are now initiated by algorithms, and the resulting data streams grow at a scale that would have been unthinkable even two decades ago. Such velocity and complexity expose the limits of traditional econometric models and open the door to systems capable of learning, adapting, and anticipating nonlinear dynamics. This paper surveys empirical, publicly available research from 2018–2025 on the deployment of machine learning (ML) and deep learning (DL) across automated trading, macroeconomic analysis, and market forecasting. Evidence is synthesized across methods ranging from tree ensembles and recurrent architectures to CNN-based microstructure models, transformer-based time-series learners, graph neural networks for global interdependence, and reinforcement learning for execution and portfolio optimization. Performance is evaluated in statistical terms and economic utility—i.e. Sharpe ratios, drawdowns, turnover, and robustness under shifting regimes.

This review highlights how ML/DL methods extract interaction effects and structural signals unavailable to ARIMA, VAR, GARCH, CAPM, and factor models, while also exposing their vulnerabilities to non-stationarity, overfitting, opacity, and adversarial instability. Cross-domain integration—where microstructure signals, firm characteristics, news sentiment, and macroeconomic indicators are fused—emerges as a central frontier, supported by hierarchical, transfer-learning, and multi-agent frameworks. Interpretability tools, causal inference techniques, stress testing, and theory-guided constraints are assessed as mechanisms that can move financial AI from raw predictive power toward transparent and economically coherent decision systems. The paper concludes by mapping future directions in neuro-symbolic finance, probabilistic forecasting, and sustainability-aligned modeling, outlining a research trajectory in which machine learning not only predicts markets but reshapes the intellectual foundations of financial analysis itself.