The aim of study was to explore the application effect and safety of comfortable nursing based on optimized mobile Internet of Things (MIoT) in the clinical sedation and diagnosis of mycoplasma pneumoniae pneumonia (MPP) in children. A total of 70 children with MPP admitted to the respiratory clinic of hospital were randomly selected and divided into a control group (comfortable nursing mode) and an observation group (comfortable nursing mode based on optimized MIoT), with 35 cases in each. The nursing effects and safety were compared between groups. The results showed that the node jitter rate, delivery success rate, and congestion times of the multilayered sensing algorithm were better than those of the mobile relay area segmentation algorithm and the wedge merge-energy hole elimination area segmentation algorithm. The CD-RISC resilience score of the observation group ((
94.72
±
1.58
) points), the proportion of children with Frankl-3 and 4 points (90%), and the comfort level ((
95.01
±
5.68
) points) were higher than those of the control group ((
64.12
±
1.62
) points, (33.33%), and (
55.23
±
6.18
) points) (
P
<
0.05
). After treatment, the proportion of children with HRCT image lesions in the observation group was lower than that in the control group (
P
<
0.05
). After treatment, the FEV1 ((
85.71
±
5.23
) % vs. (
68.26
±
5.90
) %) and FEV1/FVC ((
74.22
±
2.12
) % vs (
64.38
±
2.34
) %) of the observation group were significantly better than those of the control group (
P
<
0.05
). The results showed that the incidence of adverse reactions in the observation group (14%) was significantly lower than that in the control group (46%) (
P
<
0.05
). MIoT-assisted comfort nursing based on multilayer perception region segmentation algorithm can more effectively relieve the emotions of children in MPP outpatient department during sedation and diagnosis and treatment, improve the therapeutic effect and safety, and is worthy of clinical promotion.