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This study investigates the application of Markov and Hidden Markov Models (HMMs) for detecting latent market regimes in cryptocurrency markets, with a particular focus on Bitcoin. Cryptocurrency markets are characterized by high volatility, structural breaks, and non-stationary behavior, which often limit the effectiveness of traditional linear time-series models. Hidden Markov Models provide a probabilistic framework capable of identifying unobservable market states that generate observed price dynamics. In this research, a regime-switching framework is developed to classify Bitcoin market conditions into distinct latent states characterized by different statistical properties of returns and volatility. The proposed methodology extends standard homogeneous HMMs by incorporating non-homogeneous transition probabilities and Bayesian estimation techniques to better capture dynamic market behavior. Time-varying transition probabilities allow the model to reflect evolving market conditions influenced by trading activity and external factors. Additionally, extensions addressing duration dependence and long-memory volatility are considered to improve regime persistence modeling. Empirical evaluation using Bitcoin data demonstrates that regime-aware modeling effectively captures transitions between low-volatility consolidation phases and high-volatility turbulent periods. The results suggest that incorporating regime detection significantly improves the interpretability of market dynamics and provides a valuable foundation for risk-aware trading strategies and adaptive portfolio allocation in highly volatile digital asset markets. The findings highlight the potential of Hidden Markov frameworks as a robust tool for understanding structural shifts in cryptocurrency markets and improving predictive modeling of financial time series.