Saudi Arabia's capital market is moving from scale expansion toward deeper institutionalization, digital participation, and sophisticated trading infrastructure. Brokerage firms sit at a critical interface between investor intentions and exchange-level outcomes. This review examines how advanced decision analytics can improve brokerage operations while supporting liquidity, price discovery, resilience, and investor confidence under Vision 2030. A structured integrative review synthesized 30 peer-reviewed and institutional sources published from 2020 to 2025 across market microstructure, algorithmic and high-frequency trading, machine learning, reinforcement learning, financial risk analytics, securities regulation, and Saudi market development. Five analytics domains emerge: client and order intelligence, execution optimization, liquidity and market-making analytics, real-time risk and compliance, and operational resilience. Evidence indicates that analytics can reduce implementation shortfall, improve order routing and inventory control, and accelerate information incorporation into prices; however, benefits depend on market design, liquidity regimes, model governance, and automated participant behavior. Saudi evidence is timely because algorithmic and high-frequency traders now represent material shares of liquidity while market-making and post-trade infrastructure are expanding. The paper proposes a Saudi Brokerage Decision Analytics Framework linking governed data, predictive and prescriptive models, human oversight, execution controls, and market-quality metrics. Decision analytics should therefore be treated as a governed market capability whose success is measured jointly by brokerage efficiency and market integrity.