Although Bitcoin enables pseudonymous peer-to-peer digital transactions, its transparent public ledger architecture allows for blockchain analysis that can compromise user anonymity. Despite the presence of wallets with privacy-enhancing features, no single solution currently offers comprehensive anonymity independently. Existing privacy-preserving techniques such as CoinJoin, PayJoin, and Stealth Addresses offer differing degrees of anonymity, yet each exhibits intrinsic limitations. This study proposes a hybrid privacy architecture that integrates multiple privacy-enhancing techniques into a unified and coherent transaction workflow. By integrating decentralized CoinJoin mixing, PayJoin for input ownership obfuscation, and Stealth Addresses for unlinkable payments, the proposed model establishes a robust, privacy-oriented framework for Bitcoin transactions. The framework is implemented and evaluated through pre-funded Sparrow and JoinMarket wallets, interconnected via a fully synchronized Bitcoin Core node deployed on the testnet environment. All communications are routed via the Tor network to maintain anonymity at the network layer. Using testnet-based simulations, we evaluate the effectiveness of the architecture. The results show that combining these techniques substantially strengthens resistance to common deanonymization heuristics, enhances transaction unlinkability, and achieves higher overall anonymity than relying on individual methods alone. This demonstrates the synergistic effect of the hybrid model in providing more resilient protection against transaction tracing and blockchain surveillance.