The Internet of Things (IoT) has been envisioned as one of the key promising techniques to enable massive connectivity while guaranteeing massive ultra-reliable low-latency communications (mURLLC) requirements for time-sensitive 6G services. However, the realization of IoT has brought many new challenges, including massive connectivity, ultra low latency, super-reliability, and low power budget. Towards this end, several advanced techniques, including statistical delay-bounded quality-of-service (QoS) provisioning, cell-free (CF) massive multi-input multi-output (m-MIMO), and simultaneous wireless information and power transfer (SWIPT), have been developed to upperbound both delay and error-rate in supporting mURLLC traffics. Specifically, due to the potential benefits of favorable propagation, channel hardening, and aggressive spatial multiplexing gains, CF m-MIMO can significantly enhance the performance of SWIPT in terms of the achievable data rate and energy efficiency while improving QoS performance in 6G IoT networks. Furthermore, finite blocklength coding (FBC) has been proposed to support various massive access techniques while reducing the access latency and guaranteeing stringent QoS requirements. However, how to efficiently integrating SWIPT with CF m-MIMO using FBC based statistical delay bounded QoS theory in supporting 6G mURLLC has imposed many new challenges not encountered before. To overcome these problems, in this paper we develop statistical delay/error-rate bounded QoS provisioning schemes over SWIPT-enabled CF m-MIMO 6G IoT networks in the finite blocklength regime. In particular, we build SWIPT-enabled CF m-MIMO based system models using FBC. We also formulate and solve the -effective capacity maximization problem for statistical delay and error rate bounded QoS provisioning. Our simulation results validate and evaluate our proposed schemes in supporting mURLLC traffics.