Hierarchical Temporal Memory (HTM) offers a biologically inspired framework for machine learning, modeled after the neocortical architecture of the human brain. This project explores the fundamental principles of HTM, emphasizing its reliance on Sparse Distributed Representations (SDRs), synaptic plasticity, and temporal sequence learning. By simulating neuron activation, synaptic strength modulation, and predictive states, the HTM model aims to reproduce the neocortex’s ability to process temporal and spatial patterns. This paper outlines the functional aspects of HTM, including the reshaping of input data, matrix representation of neural states, and predictive accuracy, which collectively contribute to its capacity for learning and generalization. The implementation and analysis of HTM offer valuable insights into the development of physiologically constrained machine learning models, with implications for improving artificial intelligence systems.