The most contemporary development in information technology is the Internet of Things (IoT), which integrates the digital world with the real world. It makes it possible for things and people to communicate via the Internet. Blockchain is one of the popular topics of interest right now, and it can be used in most IoT applications. The blockchain's salient characteristics, like decentralization, data integrity, confidentiality, protection, and openness, are the main reasons for implementing it in healthcare services. Hence, this paper introduces an effective approach for storing IoT‐based healthcare data storage in the blockchain. The procedures in this suggested paradigm are as follows: user authentication, user trust verification, and optimal key storage in the blockchain. Wearable sensors are inserted in or adhered to a patient's body and used by IoT networks to gather healthcare data. These details are supplied into the verification structure constructed by adaptive dilated long short term memory with attention network (AD‐LSTM‐AN). Once the user's authentication is confirmed, then the user's trust level is computed. The same AD‐LSTM‐AN‐based verification structure is used to verify the historical data that has been stored and retrieved by the user, together with their previous transactions, in this phase. The healthcare data are then sent to the hybrid elliptic curve cryptography with attribute‐based encryption (HECC‐ABE) cryptography schemes for data encryption once the authentication of the person who attempts to store the data is confirmed. An adaptive final position‐based golden eagle‐Harris hawks optimization (AFP‐GEHHO) algorithm is used for optimizing the keys of the encrypted data. The blockchain platform stores the encrypted data of the authorized user with a high level of trust. The same process is done in reverse order whenever a user needs to recover information that is saved in the blockchain to retrieve the initially stored healthcare data. The effectiveness of secure data storage in blockchain is tested through experimental simulations. Throughout the analysis, the designed model achieves 96% in terms of accuracy. Thus, the developed model shows more secure and effective, has less error and reduces the time and memory requirements compared to existing approaches.