This chapter explores theMachine Learning (ML) intersection of classical adaptive control and modern machine learningMachine Learning (ML) through the lens of recursive least-squares (RLS)Recursive Least-Squares (RLS). Once considered a conventional tool in system identification and control, RLS is re-examined here as an early and powerful form of online learning. By drawing conceptual parallels between RLS and modern machine learningMachine Learning (ML)—particularly in the context of continual adaptationContinual adaptation, streaming data, and error minimizationMinimization—the chapter bridgesBridge historical techniques with contemporary AIArtificial Intelligence (AI) applications. Focusing on theoretical foundations, this chapter contrasts RLS with batch methods and highlights its shared goals with online algorithms like stochastic gradient descent (SGDStochastic Gradient Descent (SGD)). Despite its simplicity, RLS remains highly relevant in real-time modeling, adaptive control, and low-latency applications—especially in resource-constrained or edgeEdge computing environments. The chapter also explores hybrid frameworks combining RLS with neural networksNeural networks and Bayesian learning, showcasing its continued utility in evolving MLMachine Learning (ML) landscapes. Through examples and comparisons, readers gain insight into how RLS complements and informs the design of adaptive, interpretableInterpretable, and efficient learning systems. Ultimately, this chapter positions RLS as both a conceptual precursor and a practical tool within machine learningMachine Learning (ML), offering a foundational perspective for control engineers and MLMachine Learning (ML) practitioners seeking real-time, data-driven modeling strategies.