As with Algorithmic Trading (AT) and High-Frequency (HFT) Trading, Algorithmic Market-Making is revolutionizing the financial market microstructure. In normal markets, algorithmic systems advance price discovery, reduce bid-ask spreads, and provide continuous liquidity; however, they also introduce structural weaknesses and systemic risks largely absent in human-driven markets. This paper brings together prevailing market theories in light of current developments in the field and considers two aspects of algorithmic trading in modern capital markets: market microstructure theory, the adaptive market hypothesis, and information asymmetry theory. We propose a framework linking algorithmic speed and execution opacity to liquidity droughts, correlated feedback loops, and unexpected market disruptions. We also explore macroprudential regulatory challenges and call for dynamic circuit breakers, real-time algorithmic monitoring, and stronger board-level governance to maintain systemic stability.